Paper Digest: Recent Papers on Question Answering
Paper Digest Team extracted all recent Question Answering related papers on our radar, and generated highlight sentences for them. The results are then sorted by relevance & date. In addition to this ‘static’ page, we also provide a real-time version of this article, which has more coverage and is updated in real time to include the most recent updates on this topic.
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TABLE 1: Paper Digest: Recent Papers on Question Answering
Paper | Author(s) | Source | Date | |
---|---|---|---|---|
1 | SciQAG: A Framework for Auto-Generated Scientific Question Answering Dataset with Fine-grained Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We make the dataset, models, and evaluation codes publicly available. |
YUWEI WAN et. al. | arxiv-cs.CL | 2024-05-16 |
2 | FinTextQA: A Dataset for Long-form Financial Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work introduces FinTextQA, a novel dataset for long-form question answering (LFQA) in finance. |
JIAN CHEN et. al. | arxiv-cs.CL | 2024-05-16 |
3 | The Death of Wikipedia? — Exploring The Impact of ChatGPT on Wikipedia Engagement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore Wikipedia user metrics across four areas: page views, unique visitor numbers, edit counts and editor numbers within twelve language instances of Wikipedia. |
Neal Reeves; Wenjie Yin; Elena Simperl; Miriam Redi; | arxiv-cs.HC | 2024-05-16 |
4 | Prompting-based Synthetic Data Generation for Few-Shot Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With this motivation, we show that using large language models can improve Question Answering performance on various datasets in the few-shot setting compared to state-of-the-art approaches. For this, we perform data generation leveraging the Prompting framework, suggesting that language models contain valuable task-agnostic knowledge that can be used beyond the common pre-training/fine-tuning scheme. |
Maximilian Schmidt; Andrea Bartezzaghi; Ngoc Thang Vu; | arxiv-cs.CL | 2024-05-15 |
5 | STAR: A Benchmark for Situated Reasoning in Real-World Videos Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces a new benchmark that evaluates the situated reasoning ability via situation abstraction and logic-grounded question answering for real-world videos, called Situated Reasoning in Real-World Videos (STAR Benchmark). |
Bo Wu; Shoubin Yu; Zhenfang Chen; Joshua B Tenenbaum; Chuang Gan; | arxiv-cs.AI | 2024-05-15 |
6 | Causal Question Answering with Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Hence, in this paper, we aim to answer causal questions with a causality graph, a large-scale dataset of causal relations between noun phrases along with the relations’ provenance data. |
Lukas Bl\{u}baum; Stefan Heindorf; | www | 2024-05-13 |
7 | A Knowledge-Injected Curriculum Pretraining Framework for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose a general K nowledge-I njected C urriculum P retraining framework (KICP) to achieve comprehensive KG learning and exploitation for KBQA tasks, which is composed of knowledge injection (KI), knowledge adaptation (KA) and curriculum reasoning (CR). |
XIN LIN et. al. | www | 2024-05-13 |
8 | TANQ: An Open Domain Dataset of Table Answered Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce TANQ, the first open domain question answering dataset where the answers require building tables from information across multiple sources. |
Mubashara Akhtar; Chenxi Pang; Andreea Marzoca; Yasemin Altun; Julian Martin Eisenschlos; | arxiv-cs.CL | 2024-05-13 |
9 | FreeVA: Offline MLLM As Training-Free Video Assistant Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We aim for this work to serve as a plug-and-play, simple yet effective baseline, encouraging the direct evaluation of existing MLLMs in video domain while also standardizing the field of video conversational models to a certain extent. |
Wenhao Wu; | arxiv-cs.CV | 2024-05-13 |
10 | Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose LLMQA, a generalized framework that formulates the ODQA process into three basic steps: query expansion, document selection, and answer generation, combining the superiority of both retrieval-based and generation-based evidence. |
HONGDA SUN et. al. | www | 2024-05-13 |
11 | KET-QA: A Dataset for Knowledge Enhanced Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to use a knowledge base (KB) as the external knowledge source for TableQA and construct a dataset KET-QA with fine-grained gold evidence annotation. |
Mengkang Hu; Haoyu Dong; Ping Luo; Shi Han; Dongmei Zhang; | arxiv-cs.CL | 2024-05-13 |
12 | Faithful Temporal Question Answering Over Heterogeneous Sources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As implicit questions are sparse in prior benchmarks, we introduce a principled method for generating diverse questions. |
Zhen Jia; Philipp Christmann; Gerhard Weikum; | www | 2024-05-13 |
13 | Enhancing Complex Question Answering Over Knowledge Graphs Through Evidence Pattern Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose Evidence Pattern Retrieval (EPR) to explicitly model the structural dependencies during subgraph extraction. |
Wentao Ding; Jinmao Li; Liangchuan Luo; Yuzhong Qu; | www | 2024-05-13 |
14 | MedConceptsQA: Open Source Medical Concepts QA Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present MedConceptsQA, a dedicated open source benchmark for medical concepts question answering. |
Ofir Ben Shoham; Nadav Rappoport; | arxiv-cs.CL | 2024-05-12 |
15 | CLIP-Powered TASS: Target-Aware Single-Stream Network for Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a new CLIP-powered target-aware single-stream (TASS) network for AVQA using the image-text matching knowledge of the pretrained model through the audio-visual matching characteristic of nature. |
Yuanyuan Jiang; Jianqin Yin; | arxiv-cs.CV | 2024-05-12 |
16 | Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Since LLMs have probably seen the majority of factual question-answering datasets already, to facilitate our analysis, we proposed a fully automatic pipeline for creating a benchmark that requires knowledge of long-tail facts for answering the involved questions. |
WENYU HUANG et. al. | arxiv-cs.CL | 2024-05-10 |
17 | Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study introduces ‘clickbait spoiling’, a novel technique designed to detect, categorize, and generate spoilers as succinct text responses, countering the curiosity induced by clickbait content. |
Sayantan Pal; Souvik Das; Rohini K. Srihari; | arxiv-cs.CL | 2024-05-07 |
18 | Switchable Decision: Dynamic Neural Generation Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a switchable decision to accelerate inference by dynamically assigning computation resources for each data instance. |
Shujian Zhang; Korawat Tanwisuth; Chengyue Gong; Pengcheng He; Mingyuan Zhou; | arxiv-cs.CL | 2024-05-07 |
19 | S-EQA: Tackling Situational Queries in Embodied Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present and tackle the problem of Embodied Question Answering (EQA) with Situational Queries (S-EQA) in a household environment. |
VISHNU SASHANK DORBALA et. al. | arxiv-cs.RO | 2024-05-07 |
20 | CourseGPT-zh: An Educational Large Language Model Based on Knowledge Distillation Incorporating Prompt Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, restricted access to closed-source LLMs via APIs and the difficulty in collecting massive high-quality datasets pose obstacles to the development of large language models in education fields of various courses. Given these challenges, we propose CourseGPT-zh, a course-oriented education LLM that supports customization and low-cost deployment. |
Zheyan Qu; Lu Yin; Zitong Yu; Wenbo Wang; Xing zhang; | arxiv-cs.CL | 2024-05-07 |
21 | GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For practical deployment, it is critical to carry out knowledge distillation to preserve high performance under computational constraints. In this paper, we address a key question: given the importance of unsupervised distillation for student performance, how does one effectively ensemble knowledge from multiple teachers at this stage without the guidance of ground-truth labels? |
WENJIE ZHOU et. al. | arxiv-cs.CL | 2024-05-06 |
22 | VSA4VQA: Scaling A Vector Symbolic Architecture to Visual Question Answering on Natural Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose VSA4VQA – a novel 4D implementation of VSAs that implements a mental representation of natural images for the challenging task of Visual Question Answering (VQA). |
Anna Penzkofer; Lei Shi; Andreas Bulling; | arxiv-cs.CV | 2024-05-06 |
23 | Language-Image Models with 3D Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Multi-modal large language models (MLLMs) have shown incredible capabilities in a variety of 2D vision and language tasks. |
JANG HYUN CHO et. al. | arxiv-cs.CV | 2024-05-06 |
24 | Enhancing Contextual Understanding in Large Language Models Through Contrastive Decoding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The study addresses the open question of how LLMs effectively balance these knowledge sources during the generation process, specifically in the context of open-domain question answering. To address this issue, we introduce a novel approach integrating contrastive decoding with adversarial irrelevant passages as negative samples to enhance robust context grounding during generation. |
Zheng Zhao; Emilio Monti; Jens Lehmann; Haytham Assem; | arxiv-cs.CL | 2024-05-04 |
25 | SUKHSANDESH: An Avatar Therapeutic Question Answering Platform for Sexual Education in Rural India Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This approach aims to foster empathy and connection, which is particularly beneficial for individuals with limited literacy skills. |
Salam Michael Singh; Shubhmoy Kumar Garg; Amitesh Misra; Aaditeshwar Seth; Tanmoy Chakraborty; | arxiv-cs.CL | 2024-05-03 |
26 | OmniDrive: A Holistic LLM-Agent Framework for Autonomous Driving with 3D Perception, Reasoning and Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, capitalizing on MLLMs’ strong reasoning capabilities for improved planning behavior is challenging since planning requires full 3D situational awareness beyond 2D reasoning. To address this challenge, our work proposes a holistic framework for strong alignment between agent models and 3D driving tasks. |
SHIHAO WANG et. al. | arxiv-cs.CV | 2024-05-02 |
27 | UQA: Corpus for Urdu Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces UQA, a novel dataset for question answering and text comprehension in Urdu, a low-resource language with over 70 million native speakers. |
Samee Arif; Sualeha Farid; Awais Athar; Agha Ali Raza; | arxiv-cs.CL | 2024-05-02 |
28 | Enhanced Visual Question Answering: A Comparative Analysis and Textual Feature Extraction Via Convolutions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we conduct a comprehensive comparison between complex textual models that leverage long dependency mechanisms and simpler models focusing on local textual features within a well-established VQA framework. |
Zhilin Zhang; | arxiv-cs.CV | 2024-05-01 |
29 | LITO: Learnable Intervention for Truthfulness Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose LITO, a Learnable Intervention method for Truthfulness Optimization that automatically identifies the optimal intervention intensity tailored to a specific context. |
Farima Fatahi Bayat; Xin Liu; H. V. Jagadish; Lu Wang; | arxiv-cs.CL | 2024-04-30 |
30 | Suvach — Generated Hindi QA Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a new benchmark specifically designed for evaluating Hindi EQA models and discusses the methodology to do the same for any task. |
Vaishak Narayanan; Prabin Raj KP; Saifudheen Nouphal; | arxiv-cs.CL | 2024-04-30 |
31 | QLSC: A Query Latent Semantic Calibrator for Robust Extractive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our work introduces a novel approach, called the “Query Latent Semantic Calibrator (QLSC)”, designed as an auxiliary module for existing MRC models. |
SHENG OUYANG et. al. | arxiv-cs.CL | 2024-04-30 |
32 | When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we demonstrate how Large Language Models (LLMs) can effectively learn to use an off-the-shelf information retrieval (IR) system specifically when additional context is required to answer a given question. |
Tiziano Labruna; Jon Ander Campos; Gorka Azkune; | arxiv-cs.CL | 2024-04-30 |
33 | FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formalize three major desiderata for a fine-grained evaluation of robustness of TQA systems. |
Wei Zhou; Mohsen Mesgar; Heike Adel; Annemarie Friedrich; | arxiv-cs.CL | 2024-04-29 |
34 | Multi-Page Document Visual Question Answering Using Self-Attention Scoring Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel method and efficient training strategy for multi-page Document VQA tasks. |
Lei Kang; Rubèn Tito; Ernest Valveny; Dimosthenis Karatzas; | arxiv-cs.CV | 2024-04-29 |
35 | Multi-hop Question Answering Over Knowledge Graphs Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we evaluate the capability of (LLMs) to answer questions over KG that involve multiple hops. |
Abir Chakraborty; | arxiv-cs.AI | 2024-04-29 |
36 | TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we establish a benchmark for table visual question answering, referred to as the TableVQA-Bench, derived from pre-existing table question-answering (QA) and table structure recognition datasets. |
Yoonsik Kim; Moonbin Yim; Ka Yeon Song; | arxiv-cs.CV | 2024-04-29 |
37 | QANA: LLM-based Question Generation and Network Analysis for Zero-shot Key Point Analysis and Beyond Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Question-Answering Network Analysis (QANA), a novel opinion mining framework that utilizes Large Language Models (LLMs) to generate questions from users’ comments, constructs a bipartite graph based on the comments’ answerability to the questions, and applies centrality measures to examine the importance of opinions. |
TOMOKI FUKUMA et. al. | arxiv-cs.CL | 2024-04-28 |
38 | Can A Multichoice Dataset Be Repurposed for Extractive Question Answering? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our aim is to enable others to adapt our approach for the 120+ other language variants in Belebele, many of which are deemed under-resourced. |
TERESA LYNN et. al. | arxiv-cs.CL | 2024-04-26 |
39 | Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel customer service question-answering method that amalgamates RAG with a knowledge graph (KG). |
ZHENTAO XU et. al. | arxiv-cs.IR | 2024-04-26 |
40 | Türkçe Dil Modellerinin Performans Karşılaştırması Performance Comparison of Turkish Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Yet, despite the increasing number of these models, there is no comprehensive comparison of their performance for Turkish. This study aims to fill this gap in the literature. |
EREN DOGAN et. al. | arxiv-cs.CL | 2024-04-25 |
41 | Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a medical vision-language model that integrates large vision and language models adapted for the medical domain. |
CUONG NHAT HA et. al. | arxiv-cs.CL | 2024-04-24 |
42 | Retrieval Augmented Generation for Domain-specific Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel framework to compile a large question-answer database and develop the approach for retrieval-aware finetuning of a Large Language model. |
SANAT SHARMA et. al. | arxiv-cs.CL | 2024-04-23 |
43 | Generate-on-Graph: Treat LLM As Both Agent and KG in Incomplete Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To handle IKGQA, we propose a training-free method called Generate-on-Graph (GoG) that can generate new factual triples while exploring on KGs. |
YAO XU et. al. | arxiv-cs.CL | 2024-04-23 |
44 | Bias Patterns in The Application of LLMs for Clinical Decision Support: A Comprehensive Study Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We employ red-teaming strategies to analyze how demographics affect LLM outputs, comparing both general-purpose and clinically-trained models. |
Raphael Poulain; Hamed Fayyaz; Rahmatollah Beheshti; | arxiv-cs.CL | 2024-04-23 |
45 | Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Multimodal LLMs are the natural evolution of LLMs, and enlarge their capabilities so as to work beyond the pure textual modality. As research is being carried out to design novel architectures and vision-and-language adapters, in this paper we concentrate on endowing such models with the capability of answering questions that require external knowledge. |
DAVIDE CAFFAGNI et. al. | arxiv-cs.CV | 2024-04-23 |
46 | XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: When the right context isn’t known in advance, caching ICL can be challenging. This work addresses these limitations by introducing models that, inspired by the encoder-decoder architecture, use cross-attention to condition generation on reference text without the prompt. |
JOÃO MONTEIRO et. al. | arxiv-cs.CL | 2024-04-23 |
47 | Boter: Bootstrapping Knowledge Selection and Question Answering for Knowledge-based VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While these methods have demonstrated commendable performance in the task, they possess limitations: (1) they employ an independent retriever to acquire knowledge solely based on the similarity between the query and knowledge embeddings, without assessing whether the knowledge document is truly conducive to helping answer the question; (2) they convert the image into text and then conduct retrieval and answering in natural language space, which may not ensure comprehensive acquisition of all image information. To address these limitations, we propose Boter, a novel framework designed to bootstrap knowledge selection and question answering by leveraging the robust multimodal perception capabilities of the Multimodal Large Language Model (MLLM). |
Dongze Hao; Qunbo Wang; Longteng Guo; Jie Jiang; Jing Liu; | arxiv-cs.CV | 2024-04-22 |
48 | Pegasus-v1 Technical Report Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This technical report introduces Pegasus-1, a multimodal language model specialized in video content understanding and interaction through natural language. |
RAEHYUK JUNG et. al. | arxiv-cs.MM | 2024-04-22 |
49 | Listen Then See: Video Alignment with Speaker Attention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a cross-modal alignment and subsequent representation fusion approach that achieves state-of-the-art results (82.06\% accuracy) on the Social IQ 2.0 dataset for SIQA. |
Aviral Agrawal; Carlos Mateo Samudio Lezcano; Iqui Balam Heredia-Marin; Prabhdeep Singh Sethi; | arxiv-cs.CV | 2024-04-21 |
50 | Exploring Diverse Methods in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study explores innovative methods for improving Visual Question Answering (VQA) using Generative Adversarial Networks (GANs), autoencoders, and attention mechanisms. |
PANFENG LI et. al. | arxiv-cs.CV | 2024-04-21 |
51 | FakeBench: Uncover The Achilles’ Heels of Fake Images with Large Multimodal Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To examine the reasoning and interpretation abilities of LMMs, we present the FakeClue dataset, consisting of 15k pieces of descriptions on the telltale clues revealing the falsification of fake images. |
Yixuan Li; Xuelin Liu; Xiaoyang Wang; Shiqi Wang; Weisi Lin; | arxiv-cs.CV | 2024-04-20 |
52 | MahaSQuAD: Bridging Linguistic Divides in Marathi Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce MahaSQuAD, the first-ever full SQuAD dataset for the Indic language Marathi, consisting of 118,516 training, 11,873 validation, and 11,803 test samples. |
Ruturaj Ghatage; Aditya Kulkarni; Rajlaxmi Patil; Sharvi Endait; Raviraj Joshi; | arxiv-cs.CL | 2024-04-20 |
53 | PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Through this work, we aim to enhance the capabilities of existing vision-and-language models in handling challenges posed by text-dominant documents in VRD-QA. |
Yihao Ding; Kaixuan Ren; Jiabin Huang; Siwen Luo; Soyeon Caren Han; | arxiv-cs.CV | 2024-04-19 |
54 | LaPA: Latent Prompt Assist Model For Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the Latent Prompt Assist model (LaPA) for medical visual question answering. |
Tiancheng Gu; Kaicheng Yang; Dongnan Liu; Weidong Cai; | arxiv-cs.CV | 2024-04-19 |
55 | Evaluating AI for Law: Bridging The Gap with Open-Source Solutions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study evaluates the performance of general-purpose AI, like ChatGPT, in legal question-answering tasks, highlighting significant risks to legal professionals and clients. |
Rohan Bhambhoria; Samuel Dahan; Jonathan Li; Xiaodan Zhu; | arxiv-cs.AI | 2024-04-18 |
56 | Look, Listen, and Answer: Overcoming Biases for Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, current datasets may not provide a precise diagnostic for these methods. To tackle these challenges, firstly, we propose a novel dataset, \textit{MUSIC-AVQA-R}, crafted in two steps: rephrasing questions within the test split of a public dataset (\textit{MUSIC-AVQA}) and subsequently introducing distribution shifts to split questions. |
JIE MA et. al. | arxiv-cs.CV | 2024-04-18 |
57 | Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce Reka Core, Flash, and Edge, a series of powerful multimodal language models trained from scratch by Reka. |
AITOR ORMAZABAL et. al. | arxiv-cs.CL | 2024-04-18 |
58 | EuSQuAD: Automatically Translated and Aligned SQuAD2.0 for Basque Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work presents EuSQuAD, the first initiative dedicated to automatically translating and aligning SQuAD2.0 into Basque, resulting in more than 142k QA examples. |
Aitor García-Pablos; Naiara Perez; Montse Cuadros; | arxiv-cs.CL | 2024-04-18 |
59 | Characterizing LLM Abstention Behavior in Science QA with Context Perturbations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study the ability of LLMs to abstain from answering context-dependent science questions when provided insufficient or incorrect context. |
Bingbing Wen; Bill Howe; Lucy Lu Wang; | arxiv-cs.CL | 2024-04-18 |
60 | MedThink: Explaining Medical Visual Question Answering Via Multimodal Decision-Making Rationale Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The framework includes three distinct strategies to generate decision outcomes and corresponding rationales, thereby clearly showcasing the medical decision-making process during reasoning. |
XIAOTANG GAI et. al. | arxiv-cs.CV | 2024-04-18 |
61 | Consistency Training By Synthetic Question Generation for Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: By citing a common modeling error prevalent in previous research, we introduce a new baseline model and compare our model’s performance against it, demonstrating an improvement in results, particularly when dealing with questions that include a substantial amount of historical context. |
Hamed Hematian Hemati; Hamid Beigy; | arxiv-cs.CL | 2024-04-17 |
62 | Language Models Still Struggle to Zero-shot Reason About Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this gap, we generate a first-of-its-kind evaluation framework for time series reasoning, including formal tasks and a corresponding dataset of multi-scale time series paired with text captions across ten domains. Using these data, we probe whether language models achieve three forms of reasoning: (1) Etiological Reasoning – given an input time series, can the language model identify the scenario that most likely created it? |
Mike A. Merrill; Mingtian Tan; Vinayak Gupta; Tom Hartvigsen; Tim Althoff; | arxiv-cs.CL | 2024-04-17 |
63 | AdvisorQA: Towards Helpful and Harmless Advice-seeking Question Answering with Collective Intelligence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As the integration of large language models into daily life is on the rise, there is a clear gap in benchmarks for advising on subjective and personal dilemmas. To address this, we introduce AdvisorQA, the first benchmark developed to assess LLMs’ capability in offering advice for deeply personalized concerns, utilizing the LifeProTips subreddit forum. |
Minbeom Kim; Hwanhee Lee; Joonsuk Park; Hwaran Lee; Kyomin Jung; | arxiv-cs.CL | 2024-04-17 |
64 | Spiral of Silences: How Is Large Language Model Killing Information Retrieval? — A Case Study on Open Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we construct and iteratively run a simulation pipeline to deeply investigate the short-term and long-term effects of LLM text on RAG systems. |
XIAOYANG CHEN et. al. | arxiv-cs.IR | 2024-04-16 |
65 | ViTextVQA: A Large-Scale Visual Question Answering Dataset for Evaluating Vietnamese Text Comprehension in Images Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As a developing country, conditions are still limited, and this task is still open in Vietnam. Therefore, we introduce the first large-scale dataset in Vietnamese specializing in the ability to understand text appearing in images, we call it ViTextVQA (\textbf{Vi}etnamese \textbf{Text}-based \textbf{V}isual \textbf{Q}uestion \textbf{A}nswering dataset) which contains \textbf{over 16,000} images and \textbf{over 50,000} questions with answers. |
QUAN VAN NGUYEN et. al. | arxiv-cs.CL | 2024-04-16 |
66 | CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce an attribution-oriented Chain-of-Thought reasoning method to enhance the accuracy of attributions. |
Moshe Berchansky; Daniel Fleischer; Moshe Wasserblat; Peter Izsak; | arxiv-cs.CL | 2024-04-16 |
67 | Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision-Language Models for Selective Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose using the principle of \textit{neighborhood consistency} to identify unreliable responses from a black-box vision-language model in question answering tasks. |
Zaid Khan; Yun Fu; | arxiv-cs.CV | 2024-04-15 |
68 | Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A~Case~Study~at~HCMUT Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This article proposes a method for automatically constructing a Knowledge Graph from multiple data sources and discusses some initial applications (experimental trials) of KG in conjunction with LLMs for question-answering tasks. |
TUAN BUI et. al. | arxiv-cs.CL | 2024-04-14 |
69 | GeMQuAD : Generating Multilingual Question Answering Datasets from Large Language Models Using Few Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose GeMQuAD – a semi-supervised learning approach, extending the WeakDAP framework, applied to a dataset generated through ICL with just one example in the target language using AlexaTM 20B Seq2Seq LLM. |
Amani Namboori; Shivam Mangale; Andy Rosenbaum; Saleh Soltan; | arxiv-cs.CL | 2024-04-14 |
70 | CuriousLLM: Elevating Multi-Document QA with Reasoning-Infused Knowledge Graph Prompting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Nevertheless, the original KGP framework necessitates costly fine-tuning with large datasets yet still suffers from LLM hallucination. Therefore, we propose a reasoning-infused LLM agent to enhance this framework. |
Zukang Yang; Zixuan Zhu; | arxiv-cs.CL | 2024-04-13 |
71 | Small Models Are (Still) Effective Cross-Domain Argument Extractors Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, detailed explorations of these techniques’ ability to actually enable this transfer are lacking. In this work, we provide such a study, exploring zero-shot transfer using both techniques on six major EAE datasets at both the sentence and document levels. |
William Gantt; Aaron Steven White; | arxiv-cs.CL | 2024-04-12 |
72 | Improving Health Question Answering with Reliable and Time-Aware Evidence Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We discuss the results, highlight interesting examples, and outline challenges for future research, like managing evidence disagreement and crafting user-friendly explanations. |
Juraj Vladika; Florian Matthes; | arxiv-cs.CL | 2024-04-12 |
73 | Pretraining and Updating Language- and Domain-specific Large Language Model: A Case Study in Japanese Business Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Several previous studies have considered language- and domain-specific large language models (LLMs) as separate topics. |
Kosuke Takahashi; Takahiro Omi; Kosuke Arima; Tatsuya Ishigaki; | arxiv-cs.CL | 2024-04-12 |
74 | Synthetic Dataset Creation and Fine-Tuning of Transformer Models for Question Answering in Serbian Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we focus on generating a synthetic question answering (QA) dataset using an adapted Translate-Align-Retrieve method. |
Aleksa Cvetanović; Predrag Tadić; | arxiv-cs.CL | 2024-04-12 |
75 | Enhancing Visual Question Answering Through Question-Driven Image Captions As Prompts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a straightforward and efficient question-driven image captioning approach within this pipeline to transfer contextual information into the question-answering (QA) model. |
Övgü Özdemir; Erdem Akagündüz; | arxiv-cs.CV | 2024-04-12 |
76 | Audio Dialogues: Dialogues Dataset for Audio and Music Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing datasets for audio understanding primarily focus on single-turn interactions (i.e. audio captioning, audio question answering) for describing audio in natural language, thus limiting understanding audio via interactive dialogue. To address this gap, we introduce Audio Dialogues: a multi-turn dialogue dataset containing 163.8k samples for general audio sounds and music. |
Arushi Goel; Zhifeng Kong; Rafael Valle; Bryan Catanzaro; | arxiv-cs.CL | 2024-04-11 |
77 | LLoCO: Learning Long Contexts Offline Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce LLoCO, a technique that combines context compression, retrieval, and parameter-efficient finetuning using LoRA. |
SIJUN TAN et. al. | arxiv-cs.CL | 2024-04-11 |
78 | Multi-Image Visual Question Answering for Unsupervised Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To the best of our knowledge, we are the first to leverage a language model for unsupervised anomaly detection, for which we construct a dataset with different questions and answers. |
JUN LI et. al. | arxiv-cs.CV | 2024-04-11 |
79 | Enhancing Question Answering for Enterprise Knowledge Bases Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For the generation process, we propose a novel chain of thought (CoT) based fine-tuning method to empower the LLM-based generator to adeptly respond to user questions using retrieved documents. |
FEIHU JIANG et. al. | arxiv-cs.CL | 2024-04-10 |
80 | Text-Based Reasoning About Vector Graphics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, this failure mode persists in question-answering tasks about vector graphics — images composed purely of 2D objects and shapes. To address this challenge, we propose the Visually Descriptive Language Model (VDLM), which performs text-based reasoning about vector graphics. |
ZHENHAILONG WANG et. al. | arxiv-cs.CL | 2024-04-09 |
81 | SurveyAgent: A Conversational System for Personalized and Efficient Research Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces SurveyAgent, a novel conversational system designed to provide personalized and efficient research survey assistance to researchers. |
XINTAO WANG et. al. | arxiv-cs.CL | 2024-04-09 |
82 | LLMs’ Reading Comprehension Is Affected By Parametric Knowledge and Struggles with Hypothetical Statements Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Conversely, using data that conflicts with the models’ knowledge creates erroneous trends which distort the results. To address this issue, we suggest to use RC on imaginary data, based on fictitious facts and entities. |
Victoria Basmov; Yoav Goldberg; Reut Tsarfaty; | arxiv-cs.CL | 2024-04-09 |
83 | Identifying Shopping Intent in Product QA for Proactive Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Identifying SPQs is a challenging problem and cannot be done from question text alone, and thus requires to infer latent user behavior patterns inferred from user’s past shopping history. We propose features that capture the user’s latent shopping behavior from their purchase history, and combine them using a novel Mixture-of-Experts (MoE) model. |
BESNIK FETAHU et. al. | arxiv-cs.CL | 2024-04-09 |
84 | MoReVQA: Exploring Modular Reasoning Models for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, unlike traditional single-stage planning methods, we propose a multi-stage system consisting of an event parser, a grounding stage, and a final reasoning stage in conjunction with an external memory. |
Juhong Min; Shyamal Buch; Arsha Nagrani; Minsu Cho; Cordelia Schmid; | arxiv-cs.CV | 2024-04-09 |
85 | MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Finally, the situation is particularly grim if we consider benchmarking LLMs for languages other than English which remains, as far as we know, a totally neglected topic. In order to address these shortcomings, in this paper we present MedExpQA, the first multilingual benchmark based on medical exams to evaluate LLMs in Medical Question Answering. |
Iñigo Alonso; Maite Oronoz; Rodrigo Agerri; | arxiv-cs.CL | 2024-04-08 |
86 | Enhancing Software-Related Information Extraction Via Single-Choice Question Answering with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper describes our participation in the Shared Task on Software Mentions Disambiguation (SOMD), with a focus on improving relation extraction in scholarly texts through generative Large Language Models (LLMs) using single-choice question-answering. |
Wolfgang Otto; Sharmila Upadhyaya; Stefan Dietze; | arxiv-cs.CL | 2024-04-08 |
87 | PerkwE_COQA: Enhanced Persian Conversational Question Answering By Combining Contextual Keyword Extraction with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel method to elevate the performance of Persian Conversational question-answering (CQA) systems. |
Pardis Moradbeiki; Nasser Ghadiri; | arxiv-cs.CL | 2024-04-08 |
88 | Neural-Symbolic VideoQA: Learning Compositional Spatio-Temporal Reasoning for Real-world Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing approaches struggle to establish effective symbolic reasoning structures, which are crucial for answering compositional spatio-temporal questions. To address this challenge, we propose a neural-symbolic framework called Neural-Symbolic VideoQA (NS-VideoQA), specifically designed for real-world VideoQA tasks. |
Lili Liang; Guanglu Sun; Jin Qiu; Lizhong Zhang; | arxiv-cs.CV | 2024-04-05 |
89 | Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study investigates the capacity of sentence transformers, fine-tuned on general question-answering datasets for asymmetric semantic search, to associate descriptions of human-generated routes across Great Britain with queries often used to describe hiking experiences. |
Ilya Ilyankou; Aldo Lipani; Stefano Cavazzi; Xiaowei Gao; James Haworth; | arxiv-cs.CL | 2024-04-05 |
90 | Which Experimental Design Is Better Suited for VQA Tasks? Eye Tracking Study on Cognitive Load, Performance, and Gaze Allocations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We conducted an eye-tracking user study with 13 participants to investigate the influence of stimulus-question ordering and question modality on participants using visual question-answering (VQA) tasks. |
Sita A. Vriend; Sandeep Vidyapu; Amer Rama; Kun-Ting Chen; Daniel Weiskopf; | arxiv-cs.HC | 2024-04-05 |
91 | KazQAD: Kazakh Open-Domain Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce KazQAD — a Kazakh open-domain question answering (ODQA) dataset — that can be used in both reading comprehension and full ODQA settings, as well as for information retrieval experiments. |
Rustem Yeshpanov; Pavel Efimov; Leonid Boytsov; Ardak Shalkarbayuli; Pavel Braslavski; | arxiv-cs.CL | 2024-04-05 |
92 | TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes TinyVQA, a novel multimodal deep neural network for visual question answering tasks that can be deployed on resource-constrained tinyML hardware. |
Hasib-Al Rashid; Argho Sarkar; Aryya Gangopadhyay; Maryam Rahnemoonfar; Tinoosh Mohsenin; | arxiv-cs.CV | 2024-04-04 |
93 | Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel framework, LM-Guided CoT, that leverages a lightweight (i.e., <1B) language model (LM) for guiding a black-box large (i.e., >10B) LM in reasoning tasks. |
JOOYOUNG LEE et. al. | arxiv-cs.CL | 2024-04-04 |
94 | Using Large Language Models to Understand Telecom Standards Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we evaluate the capability of state-of-art LLMs to be used as Question Answering (QA) assistants for 3GPP document reference. |
ATHANASIOS KARAPANTELAKIS et. al. | arxiv-cs.CL | 2024-04-02 |
95 | Enhancing Human-Computer Interaction in Chest X-ray Analysis Using Vision and Language Model with Eye Gaze Patterns Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work proposes a novel approach to enhance human-computer interaction in chest X-ray analysis using Vision-Language Models (VLMs) enhanced with radiologists’ attention by incorporating eye gaze data alongside textual prompts. |
Yunsoo Kim; Jinge Wu; Yusuf Abdulle; Yue Gao; Honghan Wu; | arxiv-cs.CV | 2024-04-02 |
96 | Towards Better Generalization in Open-Domain Question Answering By Mitigating Context Memorization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the generalization performance of a retrieval-augmented QA model in two specific scenarios: 1) adapting to updated versions of the same knowledge corpus; 2) switching to completely different knowledge domains. |
Zixuan Zhang; Revanth Gangi Reddy; Kevin Small; Tong Zhang; Heng Ji; | arxiv-cs.CL | 2024-04-02 |
97 | Self-Improvement Programming for Temporal Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by semantic-parsing-based approaches that explicitly model constraints in questions by generating logical forms with symbolic operators, we design fundamental temporal operators for time constraints and introduce a novel self-improvement Programming method for TKGQA (Prog-TQA). |
ZHUO CHEN et. al. | arxiv-cs.CL | 2024-04-02 |
98 | Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a general and convenient method to covering longer contexts in Open-Domain Question-Answering tasks. |
ZHUO CHEN et. al. | arxiv-cs.CL | 2024-04-02 |
99 | Unveiling Divergent Inductive Biases of LLMs on Temporal Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite the adeptness of Large Language Models (LLMs) in discerning patterns and relationships from data, their inherent comprehension of temporal dynamics remains a formidable challenge. This research meticulously explores these intrinsic challenges within LLMs, with a specific emphasis on evaluating the performance of GPT-3.5 and GPT-4 models in the analysis of temporal data. |
Sindhu Kishore; Hangfeng He; | arxiv-cs.CL | 2024-04-01 |
100 | TraveLER: A Multi-LMM Agent Framework for Video Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, they are unable to extract details relevant to the question, instead providing general descriptions of the frame. To overcome this, we design a multi-LMM agent framework that travels along the video, iteratively collecting relevant information from keyframes through interactive question-asking until there is sufficient information to answer the question. |
Chuyi Shang; Amos You; Sanjay Subramanian; Trevor Darrell; Roei Herzig; | arxiv-cs.CV | 2024-04-01 |
101 | MChartQA: A Universal Benchmark for Multimodal Chart Question Answer Based on Vision-Language Alignment and Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional methods, which typically involve either direct multimodal processing or a table-to-text conversion followed by language model analysis, have limitations in effectively handling these complex scenarios. This paper introduces a novel multimodal chart question-answering model, specifically designed to address these intricate tasks. |
JINGXUAN WEI et. al. | arxiv-cs.CV | 2024-04-01 |
102 | VideoDistill: Language-aware Vision Distillation for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we are inspired by the human recognition and learning pattern and propose VideoDistill, a framework with language-aware (i.e., goal-driven) behavior in both vision perception and answer generation process. |
Bo Zou; Chao Yang; Yu Qiao; Chengbin Quan; Youjian Zhao; | arxiv-cs.CV | 2024-04-01 |
103 | Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Previous studies have explored using large large multimodal models (LMMs) as reward models to guide preference modeling, but their ability to accurately assess the factuality of generated responses compared to corresponding videos has not been conclusively established. This paper introduces a novel framework that utilizes detailed video captions as a proxy of video content, enabling language models to incorporate this information as supporting evidence for scoring video Question Answering (QA) predictions. |
RUOHONG ZHANG et. al. | arxiv-cs.CV | 2024-04-01 |
104 | CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We identify more advanced/explicit causal relationship modeling and joint modeling of vision and language as the immediate areas for future efforts to focus upon. |
TING EN LAM et. al. | arxiv-cs.CV | 2024-04-01 |
105 | Explainable Multi-hop Question Generation: An End-to-End Approach Without Intermediate Question Labeling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce an end-to-end question rewriting model that increases question complexity through sequential rewriting. |
Seonjeong Hwang; Yunsu Kim; Gary Geunbae Lee; | arxiv-cs.CL | 2024-03-31 |
106 | DOCMASTER: A Unified Platform for Annotation, Training, & Inference in Document Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces DOCMASTER, a unified platform designed for annotating PDF documents, model training, and inference, tailored to document question-answering. |
Alex Nguyen; Zilong Wang; Jingbo Shang; Dheeraj Mekala; | arxiv-cs.CL | 2024-03-30 |
107 | Multi-hop Question Answering Under Temporal Knowledge Editing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing models for MQA under KE exhibit poor performance when dealing with questions containing explicit temporal contexts. To address this limitation, we propose a novel framework, namely TEMPoral knowLEdge augmented Multi-hop Question Answering (TEMPLE-MQA). |
KEYUAN CHENG et. al. | arxiv-cs.CL | 2024-03-30 |
108 | How Robust Are The Tabular QA Models for Scientific Tables? A Study Using Customized Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To investigate the robustness of the existing state-of-the-art QA models on scientific hybrid tabular data, we propose a new dataset, SciTabQA, consisting of 822 question-answer pairs from scientific tables and their descriptions. |
Akash Ghosh; B Venkata Sahith; Niloy Ganguly; Pawan Goyal; Mayank Singh; | arxiv-cs.CL | 2024-03-30 |
109 | Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We hope our insights, together with future efforts within the proposed UPD settings, will enhance the broader understanding and development of more practical and reliable VLMs. |
ATSUYUKI MIYAI et. al. | arxiv-cs.CV | 2024-03-29 |
110 | Design As Desired: Utilizing Visual Question Answering for Multimodal Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we leverage descriptions in medical reports to design multi-granular question-answer pairs associated with different diseases, which assist the framework in pre-training without requiring extra annotations from experts. |
TONGKUN SU et. al. | arxiv-cs.CV | 2024-03-29 |
111 | Are Large Language Models Good at Utility Judgments? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we conduct a comprehensive study about the capabilities of LLMs in utility evaluation for open-domain QA. |
HENGRAN ZHANG et. al. | arxiv-cs.IR | 2024-03-28 |
112 | JDocQA: Japanese Document Question Answering Dataset for Generative Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce Japanese Document Question Answering (JDocQA), a large-scale document-based QA dataset, essentially requiring both visual and textual information to answer questions, which comprises 5,504 documents in PDF format and annotated 11,600 question-and-answer instances in Japanese. |
Eri Onami; Shuhei Kurita; Taiki Miyanishi; Taro Watanabe; | arxiv-cs.CL | 2024-03-28 |
113 | Retrieval-Enhanced Knowledge Editing for Multi-Hop Question Answering in Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle the problem, we propose the Retrieval-Augmented model Editing (RAE) framework tailored for multi-hop question answering. |
YUCHENG SHI et. al. | arxiv-cs.CL | 2024-03-28 |
114 | Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous Driving Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, current approaches to these systems use expensive large language model (LLM) backbones and image encoders, making such systems unsuitable for real-time autonomous driving systems where tight memory constraints exist and fast inference time is necessary. To address these previous issues, we develop EM-VLM4AD, an efficient, lightweight, multi-frame vision language model which performs Visual Question Answering for autonomous driving. |
Akshay Gopalkrishnan; Ross Greer; Mohan Trivedi; | arxiv-cs.CV | 2024-03-28 |
115 | Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a conversation-level RAG approach, which incorporates fine-grained retrieval augmentation and self-check for conversational question answering (CQA). |
LINHAO YE et. al. | arxiv-cs.AI | 2024-03-27 |
116 | MFORT-QA: Multi-hop Few-shot Open Rich Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the Multi-hop Few-shot Open Rich Table QA (MFORT-QA) approach, which consists of two major steps. |
Che Guan; Mengyu Huang; Peng Zhang; | arxiv-cs.CL | 2024-03-27 |
117 | An Image Grid Can Be Worth A Video: Zero-shot Video Question Answering Using A VLM Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we introduce a simple yet novel strategy where only a single Vision Language Model (VLM) is utilized. |
Wonkyun Kim; Changin Choi; Wonseok Lee; Wonjong Rhee; | arxiv-cs.CV | 2024-03-27 |
118 | Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here, we propose a beam-search-based most-likely prediction and a temperature-based multimodal prediction to implement both deterministic and stochastic inferences. |
Inhwan Bae; Junoh Lee; Hae-Gon Jeon; | arxiv-cs.CL | 2024-03-27 |
119 | A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we propose the Gaze-grounded VQA dataset (GazeVQA) that clarifies ambiguous questions using gaze information by focusing on a clarification process complemented by gaze information. |
Shun Inadumi; Seiya Kawano; Akishige Yuguchi; Yasutomo Kawanishi; Koichiro Yoshino; | arxiv-cs.CL | 2024-03-26 |
120 | Intrinsic Subgraph Generation for Interpretable Graph Based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce an interpretable approach for graph-based VQA and demonstrate competitive performance on the GQA dataset. |
Pascal Tilli; Ngoc Thang Vu; | arxiv-cs.CL | 2024-03-26 |
121 | GPTs and Language Barrier: A Cross-Lingual Legal QA Examination Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore the application of Generative Pre-trained Transformers (GPTs) in cross-lingual legal Question-Answering (QA) systems using the COLIEE Task 4 dataset. |
Ha-Thanh Nguyen; Hiroaki Yamada; Ken Satoh; | arxiv-cs.CL | 2024-03-26 |
122 | Can Multiple-choice Questions Really Be Useful in Detecting The Abilities of LLMs? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The misalignment between the task and the evaluation method demands a thoughtful analysis of MCQ’s efficacy, which we undertake in this paper by evaluating nine LLMs on four question-answering (QA) datasets in two languages: Chinese and English. |
WANGYUE LI et. al. | arxiv-cs.CL | 2024-03-26 |
123 | ChroniclingAmericaQA: A Large-scale Question Answering Dataset Based on Historical American Newspaper Pages Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To further contribute to advancing QA and MRC tasks and to overcome the limitation of previous datasets, we introduce ChroniclingAmericaQA, a large-scale temporal QA dataset with 487K question-answer pairs created based on the historical newspaper collection Chronicling America. |
Bhawna Piryani; Jamshid Mozafari; Adam Jatowt; | arxiv-cs.CL | 2024-03-26 |
124 | ArabicaQA: A Comprehensive Dataset for Arabic Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we address the significant gap in Arabic natural language processing (NLP) resources by introducing ArabicaQA, the first large-scale dataset for machine reading comprehension and open-domain question answering in Arabic. |
ABDELRAHMAN ABDALLAH et. al. | arxiv-cs.CL | 2024-03-26 |
125 | Denoising Table-Text Retrieval for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Previous studies in table-text open-domain question answering have two common challenges: firstly, their retrievers can be affected by false-positive labels in training datasets; secondly, they may struggle to provide appropriate evidence for questions that require reasoning across the table. To address these issues, we propose Denoised Table-Text Retriever (DoTTeR). |
Deokhyung Kang; Baikjin Jung; Yunsu Kim; Gary Geunbae Lee; | arxiv-cs.CL | 2024-03-26 |
126 | ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce ProCQA, a large-scale programming question answering dataset extracted from the StackOverflow community, offering naturally structured mixed-modal QA pairs. |
Zehan Li; Jianfei Zhang; Chuantao Yin; Yuanxin Ouyang; Wenge Rong; | arxiv-cs.CL | 2024-03-25 |
127 | Task-Agnostic Detector for Insertion-Based Backdoor Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce TABDet (Task-Agnostic Backdoor Detector), a pioneering task-agnostic method for backdoor detection. |
WEIMIN LYU et. al. | arxiv-cs.CL | 2024-03-25 |
128 | Chain-of-Action: Faithful and Multimodal Question Answering Through Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a Chain-of-Action (CoA) framework for multimodal and retrieval-augmented Question-Answering (QA). |
Zhenyu Pan; Haozheng Luo; Manling Li; Han Liu; | arxiv-cs.CL | 2024-03-25 |
129 | CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While models trained on data from mental health service platform have achieved preliminary success, challenges persist in areas such as data scarcity, quality, and ensuring a solid foundation in psychological techniques. To address these challenges, this study introduces a novel approach to enhance the precision and efficacy of psychological support through large language models. |
Hongbin Na; | arxiv-cs.CL | 2024-03-24 |
130 | Synthesize Step-by-Step: Tools, Templates and LLMs As Data Generators for Reasoning-Based Chart VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we address the lack of reasoning ability by data augmentation. |
Zhuowan Li; Bhavan Jasani; Peng Tang; Shabnam Ghadar; | arxiv-cs.CV | 2024-03-24 |
131 | Explore Until Confident: Efficient Exploration for Embodied Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider the problem of Embodied Question Answering (EQA), which refers to settings where an embodied agent such as a robot needs to actively explore an environment to gather information until it is confident about the answer to a question. In this work, we leverage the strong semantic reasoning capabilities of large vision-language models (VLMs) to efficiently explore and answer such questions. |
ALLEN Z. REN et. al. | arxiv-cs.RO | 2024-03-23 |
132 | Imagination Augmented Generation: Learning to Imagine Richer Context for Question Answering Over Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recent works indicate that LLMs have modeled rich knowledge, albeit not effectively triggered or activated. Inspired by this, we propose a novel knowledge-augmented framework, Imagination-Augmented-Generation (IAG), which simulates the human capacity to compensate for knowledge deficits while answering questions solely through imagination, without relying on external resources. |
HUANXUAN LIAO et. al. | arxiv-cs.CL | 2024-03-22 |
133 | Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models Through Question Complexity Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs from the simplest to the most sophisticated ones based on the query complexity. |
Soyeong Jeong; Jinheon Baek; Sukmin Cho; Sung Ju Hwang; Jong C. Park; | arxiv-cs.CL | 2024-03-21 |
134 | Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose an adaptive multi-agent system, named Multi-Agent VQA, to overcome the limitations of foundation models in object detection and counting by using specialized agents as tools. |
Bowen Jiang; Zhijun Zhuang; Shreyas S. Shivakumar; Dan Roth; Camillo J. Taylor; | arxiv-cs.CV | 2024-03-21 |
135 | Ranking Distillation for Open-Ended Video Question Answering with Insufficient Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result, existing works tend to directly treat all the unlabeled answers as negative labels, leading to limited ability for generalization. In this work, we introduce a simple yet effective ranking distillation framework (RADI) to mitigate this problem without additional manual annotation. |
Tianming Liang; Chaolei Tan; Beihao Xia; Wei-Shi Zheng; Jian-Fang Hu; | arxiv-cs.CV | 2024-03-21 |
136 | Large Language Models for Multi-Choice Question Classification of Medical Subjects Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The aim of this paper is to evaluate whether large language models trained on multi-choice question data can be used to discriminate between medical subjects. |
Víctor Ponce-López; | arxiv-cs.CL | 2024-03-21 |
137 | FIT-RAG: Black-Box RAG with Factual Information and Token Reduction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Simply concatenating all the retrieved documents brings large amounts of unnecessary tokens for LLMs, which degenerates the efficiency of black-box RAG. To address these issues, this paper proposes a novel black-box RAG framework which utilizes the factual information in the retrieval and reduces the number of tokens for augmentation, dubbed FIT-RAG. |
YUREN MAO et. al. | arxiv-cs.CL | 2024-03-21 |
138 | Language Repository for Long Video Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a Language Repository (LangRepo) for LLMs, that maintains concise and structured information as an interpretable (i.e., all-textual) representation. |
Kumara Kahatapitiya; Kanchana Ranasinghe; Jongwoo Park; Michael S. Ryoo; | arxiv-cs.CV | 2024-03-21 |
139 | Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, as context quality during training increases, FiD models tend to attend more uniformly to each passage in context. |
Kosuke Akimoto; Kunihiro Takeoka; Masafumi Oyamada; | arxiv-cs.CL | 2024-03-21 |
140 | Improved Baselines for Data-efficient Perceptual Augmentation of LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While different approaches have been explored to interface LLMs with “perceptual backbones” that process, e.g., visual or audio data, they are often explored for different tasks, different datasets, and using different perceptual backbones and language models, hindering direct comparison of the interfacing mechanisms. To remedy this lack of comparability between methods, we present an extensive experimental evaluation of different interfacing mechanisms, across multiple tasks (including image, video, and audio captioning as well as visual question answering), datasets and backbones, paying special attention to low-data settings. |
Théophane Vallaeys; Mustafa Shukor; Matthieu Cord; Jakob Verbeek; | arxiv-cs.CV | 2024-03-20 |
141 | Encode Once and Decode in Parallel: Efficient Transformer Decoding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a new configuration for encoder-decoder models that improves efficiency on structured output and question-answering tasks where multiple outputs are required of a single input. |
BO-RU LU et. al. | arxiv-cs.CL | 2024-03-19 |
142 | WoLF: Wide-scope Large Language Model Framework for CXR Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, to address the aforementioned caveats, we introduce WoLF, a Wide-scope Large Language Model Framework for CXR understanding. |
Seil Kang; Donghyun Kim; Junhyeok Kim; Hyo Kyung Lee; Seong Jae Hwang; | arxiv-cs.AI | 2024-03-19 |
143 | Dr3: Ask Large Language Models Not to Give Off-Topic Answers in Open Domain Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This issue of off-topic answers accounts for approximately one-third of incorrect answers, yet remains underexplored despite its significance. To alleviate this issue, we propose the Discriminate->Re-Compose->Re- Solve->Re-Decompose (Dr3) mechanism. |
YUAN GAO et. al. | arxiv-cs.CL | 2024-03-18 |
144 | Syn-QA2: Evaluating False Assumptions in Long-tail Questions with Synthetic QA Datasets Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we introduce Syn-(QA)$^2$, a set of two synthetically generated QA datasets: one generated using perturbed relations from Wikidata, and the other by perturbing HotpotQA (Yang et al. 2018). |
Ashwin Daswani; Rohan Sawant; Najoung Kim; | arxiv-cs.CL | 2024-03-18 |
145 | Enhancing Event Causality Identification with Rationale and Structure-Aware Causal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a multi-task learning framework to enhance event causality identification with rationale and structure-aware causal question answering. |
Baiyan Zhang; Qin Chen; Jie Zhou; Jian Jin; Liang He; | arxiv-cs.CL | 2024-03-17 |
146 | Knowledge Condensation and Reasoning for Knowledge-based VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the challenge, we propose two synergistic models: Knowledge Condensation model and Knowledge Reasoning model. |
DONGZE HAO et. al. | arxiv-cs.CV | 2024-03-15 |
147 | Few-Shot Image Classification and Segmentation As Visual Question Answering Using Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce the Vision-Instructed Segmentation and Evaluation (VISE) method that transforms the FS-CS problem into the Visual Question Answering (VQA) problem, utilising Vision-Language Models (VLMs), and addresses it in a training-free manner. |
Tian Meng; Yang Tao; Ruilin Lyu; Wuliang Yin; | arxiv-cs.CV | 2024-03-15 |
148 | Lost in Overlap: Exploring Watermark Collision in LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study focuses on dual watermark collisions, where two watermarks are present simultaneously in the same text. |
Yiyang Luo; Ke Lin; Chao Gu; | arxiv-cs.CL | 2024-03-15 |
149 | Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a multimodal adversarial training architecture with spatial awareness capabilities. |
Zhixuan Shen; Haonan Luo; Sijia Li; Tianrui Li; | arxiv-cs.CV | 2024-03-14 |
150 | DAM: Dynamic Adapter Merging for Continual Video QA Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a parameter-efficient method for continual video question-answering (VidQA) learning. |
FENG CHENG et. al. | arxiv-cs.CV | 2024-03-13 |
151 | MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To rectify the absence of factual evaluation, we present MoleculeQA, a novel question answering (QA) dataset which possesses 62K QA pairs over 23K molecules. |
XINGYU LU et. al. | arxiv-cs.CL | 2024-03-12 |
152 | Beyond Memorization: The Challenge of Random Memory Access in Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the mechanisms underlying knowledge storage and memory access within their parameters remain elusive. In this paper, we investigate whether a generative LM (e.g., GPT-2) is able to access its memory sequentially or randomly. |
TONGYAO ZHU et. al. | arxiv-cs.CL | 2024-03-12 |
153 | Answering Diverse Questions Via Text Attached with Key Audio-Visual Clues Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Indeed, the natural heterogeneous relationship between audiovisuals and text makes the perfect fusion challenging, to prevent high-level audio-visual semantics from weakening the network’s adaptability to diverse question types, we propose a framework for performing mutual correlation distillation (MCD) to aid question inference. |
Qilang Ye; Zitong Yu; Xin Liu; | arxiv-cs.CV | 2024-03-11 |
154 | Exploring The Impact of ChatGPT on Student Interactions in Computer-Supported Collaborative Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper takes an initial step in exploring the applicability of ChatGPT in a computer-supported collaborative learning (CSCL) environment. |
HAN KYUL KIM et. al. | arxiv-cs.CY | 2024-03-11 |
155 | KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) with ranking and re-ranking techniques, aiming to improve free-text question-answering (QA) in the medical domain. |
RUI YANG et. al. | arxiv-cs.CL | 2024-03-09 |
156 | Debiasing Multimodal Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our empirical experiments underscore the persistence of this bias, as LVLMs often provide confident answers even in the absence of relevant images or given incongruent visual input. To rectify these biases and redirect the model’s focus toward vision information, we introduce two simple, training-free strategies. |
YI-FAN ZHANG et. al. | arxiv-cs.CV | 2024-03-08 |
157 | HaluEval-Wild: Evaluating Hallucinations of Language Models in The Wild Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent benchmarks designed to assess LLM hallucinations within conventional NLP tasks, such as knowledge-intensive question answering (QA) and summarization, are insufficient for capturing the complexities of user-LLM interactions in dynamic, real-world settings. To address this gap, we introduce HaluEval-Wild, the first benchmark specifically designed to evaluate LLM hallucinations in the wild. |
Zhiying Zhu; Yiming Yang; Zhiqing Sun; | arxiv-cs.CL | 2024-03-07 |
158 | CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although existing Multimodal Large Language Models (MLLMs) can respond to audio-visual content, these responses are sometimes ambiguous and fail to describe specific audio-visual events. To overcome this limitation, we introduce the CAT, which enhances MLLM in three ways: 1) besides straightforwardly bridging audio and video, we design a clue aggregator that aggregates question-related clues in dynamic audio-visual scenarios to enrich the detailed knowledge required for large language models. |
QILANG YE et. al. | arxiv-cs.CV | 2024-03-07 |
159 | SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a novel evaluative benchmark named \textbf{SnapNTell}, specifically tailored for entity-centric VQA. |
JIELIN QIU et. al. | arxiv-cs.CV | 2024-03-07 |
160 | Can’t Remember Details in Long Documents? You Need Some R&R Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Long-context large language models (LLMs) hold promise for tasks such as question-answering (QA) over long documents, but they tend to miss important information in the middle of context documents (arXiv:2307.03172v3). Here, we introduce $\textit{R&R}$ — a combination of two novel prompt-based methods called $\textit{reprompting}$ and $\textit{in-context retrieval}$ (ICR) — to alleviate this effect in document-based QA. |
Devanshu Agrawal; Shang Gao; Martin Gajek; | arxiv-cs.CL | 2024-03-07 |
161 | Benchmarking Hallucination in Large Language Models Based on Unanswerable Math Word Problem Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a new method for evaluating LLM hallucination in Question Answering (QA) based on the unanswerable math word problem (MWP). |
YUHONG SUN et. al. | arxiv-cs.CL | 2024-03-06 |
162 | Are Language Models Puzzle Prodigies? Algorithmic Puzzles Unveil Serious Challenges in Multimodal Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces the novel task of multimodal puzzle solving, framed within the context of visual question-answering. |
Deepanway Ghosal; Vernon Toh Yan Han; Chia Yew Ken; Soujanya Poria; | arxiv-cs.CV | 2024-03-06 |
163 | Evaluating The Elementary Multilingual Capabilities of Large Language Models with MultiQ Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recent research shows that, despite limits in their intended use, people prompt LLMs in many different languages. Therefore, in this paper, we investigate the basic multilingual capabilities of state-of-the-art open LLMs beyond their intended use. |
Carolin Holtermann; Paul Röttger; Timm Dill; Anne Lauscher; | arxiv-cs.CL | 2024-03-06 |
164 | Enhancing Generalization in Medical Visual Question Answering Tasks Via Gradient-Guided Model Perturbation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a method that incorporates gradient-guided parameter perturbations to the visual encoder of the multimodality model during both pre-training and fine-tuning phases, to improve model generalization for downstream medical VQA tasks. |
Gang Liu; Hongyang Li; Zerui He; Shenjun Zhong; | arxiv-cs.CV | 2024-03-05 |
165 | CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nevertheless, there is only a little attention to use existing background knowledge for reasoning about partially observed scenes to answer questions about the scene. Yet, we as humans use such knowledge frequently to infer plausible answers to visual questions (by eliminating all inconsistent ones). |
Savitha Sam Abraham; Marjan Alirezaie; Luc De Raedt; | arxiv-cs.AI | 2024-03-05 |
166 | Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These include reduced evidence density due to duplicated entities or relationships, and reduced evidence clarity due to an inability to emphasize crucial evidence. To address these issues, we propose EFSum, an Evidence-focused Fact Summarization framework for enhanced QA with knowledge-augmented LLMs. |
Sungho Ko; Hyunjin Cho; Hyungjoo Chae; Jinyoung Yeo; Dongha Lee; | arxiv-cs.CL | 2024-03-05 |
167 | Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Medical vision-language models (VLMs) combine computer vision and natural language processing to analyze visual and textual medical data. |
Iryna Hartsock; Ghulam Rasool; | arxiv-cs.CV | 2024-03-04 |
168 | KorMedMCQA: Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce KorMedMCQA, the first Korean multiple-choice question answering (MCQA) benchmark derived from Korean healthcare professional licensing examinations, covering from the year 2012 to year 2023. |
Sunjun Kweon; Byungjin Choi; Minkyu Kim; Rae Woong Park; Edward Choi; | arxiv-cs.CL | 2024-03-03 |
169 | Brilla AI: AI Contestant for The National Science and Maths Quiz Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The NSMQ is an annual live science and mathematics competition for senior secondary school students in Ghana in which 3 teams of 2 students compete by answering questions across biology, chemistry, physics, and math in 5 rounds over 5 progressive stages until a winning team is crowned for that year. In this work, we built Brilla AI, an AI contestant that we deployed to unofficially compete remotely and live in the Riddles round of the 2023 NSMQ Grand Finale, the first of its kind in the 30-year history of the competition. |
GEORGE BOATENG et. al. | arxiv-cs.CL | 2024-03-03 |
170 | Improving Cross-lingual Representation for Semantic Retrieval with Code-switching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this work, we propose an Alternative Cross-lingual PTM for SR via code-switching. |
MIERADILIJIANG MAIMAITI et. al. | arxiv-cs.CL | 2024-03-02 |
171 | LocalRQA: From Generating Data to Locally Training, Testing, and Deploying Retrieval-Augmented QA Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose LocalRQA, an open-source toolkit that features a wide selection of model training algorithms, evaluation methods, and deployment tools curated from the latest research. |
Xiao Yu; Yunan Lu; Zhou Yu; | arxiv-cs.CL | 2024-03-01 |
172 | Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering Based on Human Reading Process Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we introduce a Prompting Explicit and Implicit knowledge (PEI) framework, which uses prompts to connect explicit and implicit knowledge, aligning with human reading process for multi-hop QA. |
Guangming Huang; Yunfei Long; Cunjin Luo; Jiaxing Shen; Xia Sun; | arxiv-cs.CL | 2024-02-29 |
173 | Let LLMs Take on The Latest Challenges! A Chinese Dynamic Question Answering Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To promote the improvement of Chinese LLMs’ ability to answer dynamic questions, in this paper, we introduce CDQA, a Chinese Dynamic QA benchmark containing question-answer pairs related to the latest news on the Chinese Internet. |
ZHIKUN XU et. al. | arxiv-cs.CL | 2024-02-29 |
174 | Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce Bonito, an open-source model for conditional task generation: the task of converting unannotated text into task-specific training datasets for instruction tuning. |
Nihal V. Nayak; Yiyang Nan; Avi Trost; Stephen H. Bach; | arxiv-cs.CL | 2024-02-28 |
175 | Can GPT Improve The State of Prior Authorization Via Guideline Based Automated Question Answering? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we evaluate whether GPT can validate numerous key factors, in turn helping health plans reach a decision drastically faster. |
Shubham Vatsal; Ayush Singh; Shabnam Tafreshi; | arxiv-cs.CL | 2024-02-28 |
176 | A Cognitive Evaluation Benchmark of Image Reasoning and Description for Large Vision Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the prevalent use of the Cookie Theft task in human cognition test, we propose a novel evaluation benchmark to evaluate high-level cognitive ability of LVLMs using images with rich semantics. |
Xiujie Song; Mengyue Wu; Kenny Q. Zhu; Chunhao Zhang; Yanyi Chen; | arxiv-cs.AI | 2024-02-28 |
177 | The First Place Solution of WSDM Cup 2024: Leveraging Large Language Models for Conversational Multi-Doc QA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce our winning approach for the Conversational Multi-Doc QA challenge in WSDM Cup 2024, which exploits the superior natural language understanding and generation capability of Large Language Models (LLMs). |
Yiming Li; Zhao Zhang; | arxiv-cs.CL | 2024-02-28 |
178 | Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, the lack of reference explanations means we cannot easily evaluate the reasoning of model decisions, a crucial component of supporting doctors in making complex medical decisions. To address these challenges, we construct two new datasets: JAMA Clinical Challenge and Medbullets. |
Hanjie Chen; Zhouxiang Fang; Yash Singla; Mark Dredze; | arxiv-cs.CL | 2024-02-28 |
179 | Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for LLM Web Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present Researchy Questions, a dataset of search engine queries tediously filtered to be non-factoid, “decompositional” and multi-perspective. |
CORBY ROSSET et. al. | arxiv-cs.CL | 2024-02-27 |
180 | JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unlike previous methods in RAG where the retrieval model was trained separately from the LLM, we introduce JMLR (for Jointly trains LLM and information Retrieval (IR)) during the fine-tuning phase. |
Junda Wang; Zhichao Yang; Zonghai Yao; Hong Yu; | arxiv-cs.CL | 2024-02-27 |
181 | BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce BlendSQL, a superset of SQLite to act as a unified dialect for orchestrating reasoning across both unstructured and structured data. |
Parker Glenn; Parag Pravin Dakle; Liang Wang; Preethi Raghavan; | arxiv-cs.CL | 2024-02-27 |
182 | Unsupervised Multiple Choices Question Answering Via Universal Corpus Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework designed to generate synthetic MCQA data barely based on contexts from the universal domain without relying on any form of manual annotation. |
Qin Zhang; Hao Ge; Xiaojun Chen; Meng Fang; | arxiv-cs.CL | 2024-02-27 |
183 | REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite the extensive efforts on RAG research, in existing methods, LLMs cannot precisely assess the relevance of retrieved documents, thus likely leading to misleading or even incorrect utilization of external knowledge (i.e., retrieved documents). To address this issue, in this paper, we propose REAR, a RElevance-Aware Retrieval-augmented approach for open-domain question answering (QA). |
YUHAO WANG et. al. | arxiv-cs.CL | 2024-02-27 |
184 | Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and Sundanese Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we investigate the effectiveness of using LLMs in generating culturally relevant commonsense QA datasets for Indonesian and Sundanese languages. |
Rifki Afina Putri; Faiz Ghifari Haznitrama; Dea Adhista; Alice Oh; | arxiv-cs.CL | 2024-02-27 |
185 | Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we suggest investigating internal activations and quantifying LLM’s truthfulness using the local intrinsic dimension (LID) of model activations. |
Fan Yin; Jayanth Srinivasa; Kai-Wei Chang; | arxiv-cs.CL | 2024-02-27 |
186 | Spoken Question Answering and Speech Continuation Using Spectrogram-Powered LLM Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present Spectron, a novel approach to adapting pre-trained large language models (LLMs) to perform spoken question answering (QA) and speech continuation. |
ELIYA NACHMANI et. al. | iclr | 2024-02-26 |
187 | SuRe: Summarizing Retrievals Using Answer Candidates for Open-domain QA of LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we design a simple yet effective framework to enhance open-domain QA (ODQA) with LLMs, based on the summarized retrieval (SuRe). |
JAEHYUNG KIM et. al. | iclr | 2024-02-26 |
188 | A Framework for Inference Inspired By Human Memory Mechanisms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by human brain’s memory system and cognitive architectures, we propose a PMI framework that consists of perception, memory and inference components. |
Xiangyu Zeng; Jie Lin; Piao Hu; Ruizheng Huang; Zhicheng Zhang; | iclr | 2024-02-26 |
189 | Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With augmentation of retrieval module, open-source Large Language Models (LLMs) can produce coherent answers often with different focuses, but are still sub-optimal in terms of reliable evidence selection and in-depth question analysis. In this paper, we propose a novel Chain-of-Discussion framework to leverage the synergy among multiple open-source LLMs aiming to provide \textbf{more correct} and \textbf{more comprehensive} answers for open-ended QA, although they are not strong enough individually. |
Mingxu Tao; Dongyan Zhao; Yansong Feng; | arxiv-cs.CL | 2024-02-26 |
190 | SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose SelfCheck, a general-purpose zero-shot verification schema for recognizing such errors. |
Ning Miao; Yee Whye Teh; Tom Rainforth; | iclr | 2024-02-26 |
191 | Pre-training Cross-lingual Open Domain Question Answering with Large-scale Synthetic Supervision Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we show that CLQA can be addressed using a single encoder-decoder model. |
Fan Jiang; Tom Drummond; Trevor Cohn; | arxiv-cs.CL | 2024-02-26 |
192 | RAPPER: Reinforced Rationale-Prompted Paradigm for Natural Language Explanation in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In practice, one might encounter explanations which lack informativeness or contradict visual-grounded facts, known as implausibility and hallucination problems, respectively. To tackle these challenging issues, we consider the task of visual question answering (VQA) and introduce Rapper, a two-stage Reinforced Rationale-Prompted Paradigm. |
KAI-PO CHANG et. al. | iclr | 2024-02-26 |
193 | CABINET: Content Relevance-based Noise Reduction for Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To mitigate this, we propose CABINET (Content RelevAnce-Based NoIse ReductioN for TablE QuesTion-Answering) – a framework to enable LLMs to focus on relevant tabular data by suppressing extraneous information.We release our code and datasets here. |
SOHAN PATNAIK et. al. | iclr | 2024-02-26 |
194 | Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We identify and address several reliability challenges in existing QG/A work: (a) QG questions should respect the prompt (avoiding hallucinations, duplications, and omissions) and (b) VQA answers should be consistent (not asserting that there is no motorcycle in an image while also claiming the motorcycle is blue). We address these issues with Davidsonian Scene Graph (DSG), an empirically grounded evaluation framework inspired by formal semantics, which is adaptable to any QG/A frameworks. |
JAEMIN CHO et. al. | iclr | 2024-02-26 |
195 | Bootstrapping Variational Information Pursuit with Large Language and Vision Models for Interpretable Image Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This limits V-IP’s application to small-scale tasks where manual data annotation is feasible. In this work, we focus on image classification tasks and propose to relieve this bottleneck by leveraging pretrained language and vision models. |
Aditya Chattopadhyay; Kwan Ho Ryan Chan; Rene Vidal; | iclr | 2024-02-26 |
196 | Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore how different prompting strategies influence LLM confidence calibration and how it could be improved. |
XINRAN ZHAO et. al. | arxiv-cs.CL | 2024-02-26 |
197 | The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of The Open World IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present the All-Seeing (AS) project: a large-scale dataset and model for recognizing and understanding everything in the open world.Using a scalable data engine that incorporates human feedback and efficient models in the loop, we create a new dataset (AS-1B) with over 1.2 billion regions annotated with semantic tags, question-answering pairs, and detailed captions. |
WEIYUN WANG et. al. | iclr | 2024-02-26 |
198 | INSIDE: LLMs’ Internal States Retain The Power of Hallucination Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, we propose to explore the dense semantic information retained within LLMs’ \textbf{IN}ternal \textbf{S}tates for halluc\textbf{I}nation \textbf{DE}tection (\textbf{INSIDE}). |
CHAO CHEN et. al. | iclr | 2024-02-26 |
199 | EQA-MX: Embodied Question Answering Using Multimodal Expression Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we have introduced 8 novel embodied question answering (EQA) tasks to develop learning models to comprehend embodied questions with multimodal expressions.We have developed a novel large-scale dataset, EQA-MX, with over 8 million diverse embodied QA data samples involving multimodal expressions from multiple visual and verbal perspectives. |
Md Mofijul Islam; Alexi Gladstone; Riashat Islam; Tariq Iqbal; | iclr | 2024-02-26 |
200 | Tailoring Self-Rationalizers with Multi-Reward Distillation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we enable small-scale LMs (∼200x smaller than GPT-3) to generate rationales that not only improve downstream task performance, but are also more plausible, consistent, and diverse, assessed both by automatic and human evaluation. |
SAHANA RAMNATH et. al. | iclr | 2024-02-26 |
201 | EHRNoteQA: A Patient-Specific Question Answering Benchmark for Evaluating Large Language Models in Clinical Settings Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This study introduces EHRNoteQA, a novel patient-specific question answering benchmark tailored for evaluating Large Language Models (LLMs) in clinical environments. |
SUNJUN KWEON et. al. | arxiv-cs.CL | 2024-02-25 |
202 | Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Research Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Models such as BERT and GPT-3, trained on vast amounts of data, have revolutionized language understanding and generation. These pre-trained models serve as robust bases for various tasks including semantic understanding, intelligent writing, and reasoning, paving the way for a more generalized form of artificial intelligence. |
Shuning Huo; Yafei Xiang; Hanyi Yu; Mengran Zhu; Yulu Gong; | arxiv-cs.CL | 2024-02-25 |
203 | PerLTQA: A Personal Long-Term Memory Dataset for Memory Classification, Retrieval, and Synthesis in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on PerLTQA, we propose a novel framework for memory integration and generation, consisting of three main components: Memory Classification, Memory Retrieval, and Memory Synthesis. |
YIMING DU et. al. | arxiv-cs.CL | 2024-02-25 |
204 | Bridging The Gap Between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Integrating proposed mechanisms above, we present BridgeQA, that offers a fresh perspective on multi-modal transformer-based architectures for 3D-VQA. |
Wentao Mo; Yang Liu; | arxiv-cs.CV | 2024-02-24 |
205 | Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Yet, fully leveraging LLMs to parse questions into logical forms in low-resource scenarios poses a substantial challenge. To tackle these hurdles, we introduce Interactive-KBQA, a framework designed to generate logical forms through direct interaction with knowledge bases (KBs). |
Guanming Xiong; Junwei Bao; Wen Zhao; | arxiv-cs.CL | 2024-02-23 |
206 | Biomedical Entity Linking As Multiple Choice Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although biomedical entity linking (BioEL) has made significant progress with pre-trained language models, challenges still exist for fine-grained and long-tailed entities. To address these challenges, we present BioELQA, a novel model that treats Biomedical Entity Linking as Multiple Choice Question Answering. |
Zhenxi Lin; Ziheng Zhang; Xian Wu; Yefeng Zheng; | arxiv-cs.CL | 2024-02-23 |
207 | CommVQA: Situating Visual Question Answering in Communicative Contexts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To evaluate how situating images within naturalistic contexts shapes visual questions, we introduce CommVQA, a VQA dataset consisting of images, image descriptions, real-world communicative scenarios where the image might appear (e.g., a travel website), and follow-up questions and answers conditioned on the scenario. |
Nandita Shankar Naik; Christopher Potts; Elisa Kreiss; | arxiv-cs.CL | 2024-02-22 |
208 | Leveraging Large Language Models for Concept Graph Recovery and Question Answering in NLP Education Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To tackle TutorQA queries, we present CGLLM, a pipeline integrating concept graphs with LLMs for answering diverse questions. |
RUI YANG et. al. | arxiv-cs.CL | 2024-02-22 |
209 | Triad: A Framework Leveraging A Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present Triad, a unified framework that utilizes an LLM-based agent with three roles for KBQA tasks. |
CHANG ZONG et. al. | arxiv-cs.CL | 2024-02-22 |
210 | Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the Word-Sequence Entropy (WSE), which calibrates the uncertainty proportion at both the word and sequence levels according to the semantic relevance, with greater emphasis placed on keywords and more relevant sequences when performing uncertainty quantification. |
ZHIYUAN WANG et. al. | arxiv-cs.CL | 2024-02-21 |
211 | PQA: Zero-shot Protein Question Answering for Free-form Scientific Enquiry with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce the novel task of zero-shot Protein Question Answering (PQA) for free-form scientific enquiry. |
Eli M Carrami; Sahand Sharifzadeh; | arxiv-cs.LG | 2024-02-21 |
212 | FanOutQA: Multi-Hop, Multi-Document Question Answering for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To evaluate complex reasoning in LLMs more fully, we present FanOutQA, a high-quality dataset of fan-out question-answer pairs and human-annotated decompositions with English Wikipedia as the knowledge base. |
Andrew Zhu; Alyssa Hwang; Liam Dugan; Chris Callison-Burch; | arxiv-cs.CL | 2024-02-21 |
213 | Exploring The Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we address this research gap in two steps. |
DEHAI MIN et. al. | arxiv-cs.CL | 2024-02-20 |
214 | Slot-VLM: SlowFast Slots for Video-Language Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce Slot-VLM, a novel framework designed to generate semantically decomposed video tokens, in terms of object-wise and event-wise visual representations, to facilitate LLM inference. |
Jiaqi Xu; Cuiling Lan; Wenxuan Xie; Xuejin Chen; Yan Lu; | arxiv-cs.CV | 2024-02-20 |
215 | Question Calibration and Multi-Hop Modeling for Temporal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: (II) They neither emphasize the graph structure between entities nor explicitly model the multi-hop relationship in the graph, which will make it difficult to solve complex multi-hop question answering. To alleviate this problem, we propose a novel Question Calibration and Multi-Hop Modeling (QC-MHM) approach. |
Chao Xue; Di Liang; Pengfei Wang; Jing Zhang; | arxiv-cs.CL | 2024-02-20 |
216 | Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle these, we present a novel modality-aware integration with LLMs for KVQA (MAIL). |
JUNNAN DONG et. al. | arxiv-cs.CV | 2024-02-20 |
217 | BiMediX: Bilingual Medical Mixture of Experts LLM Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce BiMediX, the first bilingual medical mixture of experts LLM designed for seamless interaction in both English and Arabic. |
SARA PIERI et. al. | arxiv-cs.CL | 2024-02-20 |
218 | Benchmarking Retrieval-Augmented Generation for Medicine Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Using MIRAGE, we conducted large-scale experiments with over 1.8 trillion prompt tokens on 41 combinations of different corpora, retrievers, and backbone LLMs through the MedRAG toolkit introduced in this work. |
Guangzhi Xiong; Qiao Jin; Zhiyong Lu; Aidong Zhang; | arxiv-cs.CL | 2024-02-20 |
219 | Self-DC: When to Retrieve and When to Generate? Self Divide-and-Conquer for Compositional Unknown Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose the first Compositional unknown Question-Answering dataset (CuQA), and introduce a Self Divide-and-Conquer (Self-DC) framework to empower LLMs to adaptively call different methods on-demand, resulting in better performance and efficiency. |
HONGRU WANG et. al. | arxiv-cs.CL | 2024-02-20 |
220 | Retrieval Helps or Hurts? A Deeper Dive Into The Efficacy of Retrieval Augmentation to Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, our goal is to offer a more detailed, fact-centric analysis by exploring the effects of combinations of entities and relations. |
Seiji Maekawa; Hayate Iso; Sairam Gurajada; Nikita Bhutani; | arxiv-cs.CL | 2024-02-20 |
221 | MRKE: The Multi-hop Reasoning Evaluation of LLMs By Knowledge Edition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus we introduce an LLM MHQA evaluation benchmark, the first QA benchmark based on the new, unprecedented knowledge by editing the off-the-shelf HotpotQA dataset; Besides, we also annotate and evaluate the reasoning chain in the form of sub-questions and intermediate answers corresponding to the multi-hop questions. |
Jian Wu; Linyi Yang; Manabu Okumura; Yue Zhang; | arxiv-cs.CL | 2024-02-19 |
222 | Tables As Images? Exploring The Strengths and Limitations of LLMs on Multimodal Representations of Tabular Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the effectiveness of various LLMs in interpreting tabular data through different prompting strategies and data formats. |
NAIHAO DENG et. al. | arxiv-cs.LG | 2024-02-19 |
223 | Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without The Question? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In three MCQA datasets and four LLMs, this prompt bests a majority baseline in 11/12 cases, with up to 0.33 accuracy gain. To help explain this behavior, we conduct an in-depth, black-box analysis on memorization, choice dynamics, and question inference. |
Nishant Balepur; Abhilasha Ravichander; Rachel Rudinger; | arxiv-cs.CL | 2024-02-19 |
224 | BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs Via Key Supporting Evidence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces BIDER, an approach that refines retrieval documents into Key Supporting Evidence (KSE) through knowledge synthesis, supervised fine-tuning (SFT), and preference alignment. |
Jiajie Jin; Yutao Zhu; Yujia Zhou; Zhicheng Dou; | arxiv-cs.CL | 2024-02-19 |
225 | FormulaQA: A Question Answering Dataset for Formula-Based Numerical Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing numerical reasoning datasets seldom explicitly indicate the formulas employed during the reasoning steps. To bridge this gap, we propose a question answering dataset for formula-based numerical reasoning called FormulaQA, from junior high school physics examinations. |
XIAO LI et. al. | arxiv-cs.CL | 2024-02-19 |
226 | MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose Meaning-Aware Response Scoring (MARS) as an alternative to length-normalized scoring for UE methods. |
YAVUZ FARUK BAKMAN et. al. | arxiv-cs.CL | 2024-02-18 |
227 | Question Answering Over Spatio-Temporal Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, we propose STCQA, a new spatio-temporal KGQA approach that utilizes a novel STKG embedding method named STComplEx. |
Xinbang Dai; Huiying Li; Guilin Qi; | arxiv-cs.CL | 2024-02-18 |
228 | Learning From Failure: Integrating Negative Examples When Fine-tuning Large Language Models As Agents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Discarding failed trajectories also leads to significant wastage of data and resources and limits the possible optimization paths during fine-tuning. In this paper, we argue that unsuccessful trajectories offer valuable insights, and LLMs can learn from these trajectories through appropriate quality control and fine-tuning strategies. |
Renxi Wang; Haonan Li; Xudong Han; Yixuan Zhang; Timothy Baldwin; | arxiv-cs.CL | 2024-02-18 |
229 | Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we delve deeper into the CoT reasoning capabilities of LLMs in multi-hop question answering by utilizing knowledge graphs (KGs). |
MINH-VUONG NGUYEN et. al. | arxiv-cs.CL | 2024-02-17 |
230 | Evaluating LLMs’ Mathematical Reasoning in Financial Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The results provide insights into LLMs’ capabilities and limitations in handling complex mathematical scenarios for semi-structured tables. Ultimately, we introduce a novel prompting technique tailored to semi-structured documents, matching or outperforming other baselines in performance while providing a nuanced understanding of LLMs abilities for such a task. |
Pragya Srivastava; Manuj Malik; Vivek Gupta; Tanuja Ganu; Dan Roth; | arxiv-cs.CL | 2024-02-17 |
231 | PANDA (Pedantic ANswer-correctness Determination and Adjudication):Improving Automatic Evaluation for Question Answering and Text Generation Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Question answering (QA) can only make progress if we know if an answer is correct, but for many of the most challenging and interesting QA examples, current answer correctness … |
Zongxia Li; Ishani Mondal; Yijun Liang; Huy Nghiem; Jordan Lee Boyd-Graber; | arxiv-cs.CL | 2024-02-16 |
232 | Zero-shot Sampling of Adversarial Entities in Biomedical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we propose a powerscaled distance-weighted sampling scheme in embedding space to discover diverse adversarial entities as distractors. |
R. PATRICK XIAN et. al. | arxiv-cs.CL | 2024-02-16 |
233 | PAT-Questions: A Self-Updating Benchmark for Present-Anchored Temporal Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: PATQA poses unique challenges: (1) large language models (LLMs) may have outdated knowledge, (2) complex temporal relationships (e.g. ‘before’, ‘previous’) are hard to reason, (3) multi-hop reasoning may be required, and (4) the gold answers of benchmarks must be continuously updated. To address these challenges, we introduce the PAT-Questions benchmark, which includes single and multi-hop temporal questions. |
Jannat Ara Meem; Muhammad Shihab Rashid; Yue Dong; Vagelis Hristidis; | arxiv-cs.CL | 2024-02-16 |
234 | Exploring Hybrid Question Answering Via Program-based Prompting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose HProPro, a novel program-based prompting framework for the hybrid question answering task. |
QI SHI et. al. | arxiv-cs.CL | 2024-02-16 |
235 | VQAttack: Transferable Adversarial Attacks on Visual Question Answering Via Pre-trained Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Correspondingly, we propose a novel VQAttack model, which can iteratively generate both image and text perturbations with the designed modules: the large language model (LLM)-enhanced image attack and the cross-modal joint attack module. |
ZIYI YIN et. al. | arxiv-cs.CV | 2024-02-16 |
236 | II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose II-MMR, a novel idea to identify and improve multi-modal multi-hop reasoning in VQA. |
Jihyung Kil; Farideh Tavazoee; Dongyeop Kang; Joo-Kyung Kim; | arxiv-cs.CV | 2024-02-16 |
237 | Question-Instructed Visual Descriptions for Zero-Shot Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present Q-ViD, a simple approach for video question answering (video QA), that unlike prior methods, which are based on complex architectures, computationally expensive pipelines or use closed models like GPTs, Q-ViD relies on a single instruction-aware open vision-language model (InstructBLIP) to tackle videoQA using frame descriptions. |
David Romero; Thamar Solorio; | arxiv-cs.CV | 2024-02-16 |
238 | KnowTuning: Knowledge-aware Fine-tuning for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: These limitations stem from inadequate knowledge awareness of LLMs during vanilla fine-tuning. To address these problems, we propose a knowledge-aware fine-tuning (KnowTuning) method to improve fine-grained and coarse-grained knowledge awareness of LLMs. |
YOUGANG LYU et. al. | arxiv-cs.CL | 2024-02-16 |
239 | A Question Answering Based Pipeline for Comprehensive Chinese EHR Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel approach that automatically generates training data for transfer learning of QA models. |
Huaiyuan Ying; Sheng Yu; | arxiv-cs.CL | 2024-02-16 |
240 | GenDec: A Robust Generative Question-decomposition Method for Multi-hop Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a \textbf{gen}erative question \textbf{dec}omposition method (GenDec) from the perspective of explainable QA by generating independent and complete sub-questions based on incorporating additional extracted evidence for enhancing LLMs’ reasoning ability in RAG. |
JIAN WU et. al. | arxiv-cs.CL | 2024-02-16 |
241 | Enhancing Large Language Models with Pseudo- and Multisource- Knowledge Graphs for Open-ended Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For precise questions, we observe a minimum accuracy improvement of 7.5. |
Jiaxiang Liu; Tong Zhou; Yubo Chen; Kang Liu; Jun Zhao; | arxiv-cs.CL | 2024-02-15 |
242 | A Dataset of Open-Domain Question Answering with Multiple-Span Answers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous efforts for constructing MSQA datasets predominantly emphasized entity-centric contextualization, resulting in a bias towards collecting factoid questions and potentially overlooking questions requiring more detailed descriptive responses. To overcome these limitations, we present CLEAN, a comprehensive Chinese multi-span question answering dataset that involves a wide range of open-domain subjects with a substantial number of instances requiring descriptive answers. |
Zhiyi Luo; Yingying Zhang; Shuyun Luo; Ying Zhao; Wentao Lyu; | arxiv-cs.CL | 2024-02-15 |
243 | BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges. In this paper, we introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model and further pre-trained on PubMed Central. |
YANIS LABRAK et. al. | arxiv-cs.CL | 2024-02-15 |
244 | Answer Is All You Need: Instruction-following Text Embedding Via Answering The Question Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work aims to build a text embedder that can capture characteristics of texts specified by user instructions. |
LETIAN PENG et. al. | arxiv-cs.CL | 2024-02-14 |
245 | Pretraining Vision-Language Model for Difference Visual Question Answering in Longitudinal Chest X-rays Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we introduce a novel VLM called PLURAL, which is pretrained on natural and longitudinal chest X-ray data for the diff-VQA task. |
Yeongjae Cho; Taehee Kim; Heejun Shin; Sungzoon Cho; Dongmyung Shin; | arxiv-cs.CV | 2024-02-14 |
246 | Multi-Query Focused Disaster Summarization Via Instruction-Based Prompting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, participants are asked to develop systems that can extract key facts from several disaster-related events, which ultimately serve as a summary. This paper describes our method to tackle this challenging task. |
Philipp Seeberger; Korbinian Riedhammer; | arxiv-cs.CL | 2024-02-14 |
247 | Prompt-based Personalized Federated Learning for Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel prompt-based personalized federated learning (pFL) method to address data heterogeneity and privacy concerns in traditional medical visual question answering (VQA) methods. |
He Zhu; Ren Togo; Takahiro Ogawa; Miki Haseyama; | arxiv-cs.CV | 2024-02-14 |
248 | Visually Dehallucinative Instruction Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a novel and scalable method for generating visually dehallucinative instructions, dubbed CAP2QA, that constrains the scope to only image contents. |
Sungguk Cha; Jusung Lee; Younghyun Lee; Cheoljong Yang; | arxiv-cs.CV | 2024-02-13 |
249 | Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we systematically investigate how to robustly fine-tune LLMs for better source quality and answer attributability. |
Tobias Schimanski; Jingwei Ni; Mathias Kraus; Elliott Ash; Markus Leippold; | arxiv-cs.CL | 2024-02-13 |
250 | Visual Question Answering Instruction: Unlocking Multimodal Large Language Model To Domain-Specific Visual Multitasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We developed a method to transform domain-specific visual and vision-language datasets into a unified question answering format called Visual Question Answering Instruction (VQA-IN), thereby extending MLLM to domain-specific tasks. |
Jusung Lee; Sungguk Cha; Younghyun Lee; Cheoljong Yang; | arxiv-cs.CV | 2024-02-13 |
251 | G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In contrast, we develop a flexible question-answering framework targeting real-world textual graphs, applicable to multiple applications including scene graph understanding, common sense reasoning, and knowledge graph reasoning. |
XIAOXIN HE et. al. | arxiv-cs.LG | 2024-02-12 |
252 | T-RAG: Lessons from The LLM Trenches Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Large Language Models (LLM) have shown remarkable language capabilities fueling attempts to integrate them into applications across a wide range of domains. |
Masoomali Fatehkia; Ji Kim Lucas; Sanjay Chawla; | arxiv-cs.AI | 2024-02-12 |
253 | A Benchmark for Multi-modal Foundation Models on Low-level Vision: from Single Images to Pairs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, given that pairwise comparison can better avoid ambiguity of responses and has been adopted by many human experiments, we further extend the low-level perception-related question-answering and description evaluations of MLLMs from single images to image pairs. Specifically, for perception (A1), we carry out the LLVisionQA+ dataset, comprising 2,990 single images and 1,999 image pairs each accompanied by an open-ended question about its low-level features; for description (A2), we propose the LLDescribe+ dataset, evaluating MLLMs for low-level descriptions on 499 single images and 450 pairs. |
Zicheng Zhang; Haoning Wu; Erli Zhang; Guangtao Zhai; Weisi Lin; | arxiv-cs.CV | 2024-02-11 |
254 | Exploring Perceptual Limitation of Multimodal Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we quantitatively study the perception of small visual objects in several state-of-the-art MLLMs and reveal a pervasive limitation in answering questions about small objects in images. |
Jiarui Zhang; Jinyi Hu; Mahyar Khayatkhoei; Filip Ilievski; Maosong Sun; | arxiv-cs.CV | 2024-02-11 |
255 | FaBERT: Pre-training BERT on Persian Blogs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce FaBERT, a Persian BERT-base model pre-trained on the HmBlogs corpus, encompassing both informal and formal Persian texts. |
Mostafa Masumi; Seyed Soroush Majd; Mehrnoush Shamsfard; Hamid Beigy; | arxiv-cs.CL | 2024-02-09 |
256 | The Generative AI Paradox on Evaluation: What It Can Solve, It May Not Evaluate Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper explores the assumption that Large Language Models (LLMs) skilled in generation tasks are equally adept as evaluators. |
Juhyun Oh; Eunsu Kim; Inha Cha; Alice Oh; | arxiv-cs.CL | 2024-02-09 |
257 | EntGPT: Linking Generative Large Language Models with Knowledge Bases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The ability of Large Language Models (LLMs) to generate factually correct output remains relatively unexplored due to the lack of fact-checking and knowledge grounding during training and inference. In this work, we aim to address this challenge through the Entity Disambiguation (ED) task. |
YIFAN DING et. al. | arxiv-cs.CL | 2024-02-09 |
258 | In-Context Principle Learning from Mistakes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nonetheless, all ICL-based approaches only learn from correct input-output pairs. In this paper, we revisit this paradigm, by learning more from the few given input-output examples. |
TIANJUN ZHANG et. al. | arxiv-cs.CL | 2024-02-07 |
259 | SPARQL Generation: An Analysis on Fine-tuning OpenLLaMA for Question Answering Over A Life Science Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To overcome this challenge, in this study, we evaluate several strategies for fine-tuning the OpenLlama LLM for question answering over life science knowledge graphs. In particular, we propose an end-to-end data augmentation approach for extending a set of existing queries over a given knowledge graph towards a larger dataset of semantically enriched question-to-SPARQL query pairs, enabling fine-tuning even for datasets where these pairs are scarce. |
Julio C. Rangel; Tarcisio Mendes de Farias; Ana Claudia Sima; Norio Kobayashi; | arxiv-cs.AI | 2024-02-07 |
260 | ScreenAI: A Vision-Language Model for UI and Infographics Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce ScreenAI, a vision-language model that specializes in UI and infographics understanding. |
GILLES BAECHLER et. al. | arxiv-cs.CV | 2024-02-07 |
261 | Convincing Rationales for Visual Question Answering Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To generate both visual and textual rationales next to the predicted answer to the given image/question pair, we propose Convincing Rationales for VQA, CRVQA. |
Kun Li; George Vosselman; Michael Ying Yang; | arxiv-cs.CV | 2024-02-06 |
262 | Training Language Models to Generate Text with Citations Via Fine-grained Rewards Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose an effective training framework using fine-grained rewards to teach LLMs to generate highly supportive and relevant citations, while ensuring the correctness of their responses. |
Chengyu Huang; Zeqiu Wu; Yushi Hu; Wenya Wang; | arxiv-cs.CL | 2024-02-06 |
263 | NORMY: Non-Uniform History Modeling for Open Retrieval Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose NORMY, the first unsupervised non-uniform history modeling pipeline which generates the best conversational history for each module. |
Muhammad Shihab Rashid; Jannat Ara Meem; Vagelis Hristidis; | arxiv-cs.IR | 2024-02-06 |
264 | Text-Guided Image Clustering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Current image clustering methods, however, neglect the use of generated textual descriptions. We, therefore, propose Text-Guided Image Clustering, i.e., generating text using image captioning and visual question-answering (VQA) models and subsequently clustering the generated text. |
ANDREAS STEPHAN et. al. | arxiv-cs.LG | 2024-02-05 |
265 | LB-KBQA: Large-language-model and BERT Based Knowledge-Based Question and Answering System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, both of the methods suffer from limited resources in intent recognition. To address this issue, we propose a novel KBQA system based on a Large Language Model(LLM) and BERT (LB-KBQA). |
Yan Zhao; Zhongyun Li; Yushan Pan; Jiaxing Wang; Yihong Wang; | arxiv-cs.CL | 2024-02-05 |
266 | Enhancing Textbook Question Answering Task with Large Language Models and Retrieval Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a methodology that handle the out-of-domain scenario in TQA where concepts are spread across different lessons by incorporating the retrieval augmented generation (RAG) technique and utilize transfer learning to handle the long context and enhance reasoning abilities. |
Hessa Abdulrahman Alawwad; Areej Alhothali; Usman Naseem; Ali Alkhathlan; Amani Jamal; | arxiv-cs.CL | 2024-02-05 |
267 | Knowledge Generation for Zero-shot Knowledge-based VQA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by recent work on knowledge generation from LLMs for text-based QA, in this work we propose and test a similar knowledge-generation-based K-VQA method, which first generates knowledge from an LLM and then incorporates the generated knowledge for K-VQA in a zero-shot manner. |
Rui Cao; Jing Jiang; | arxiv-cs.CL | 2024-02-04 |
268 | Large Language Model for Table Processing: A Survey Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Tables, typically two-dimensional and structured to store large amounts of data, are essential in daily activities like database queries, spreadsheet calculations, and generating … |
Weizheng Lu; Jiaming Zhang; Jing Zhang; Yueguo Chen; | ArXiv | 2024-02-04 |
269 | GeReA: Question-Aware Prompt Captions for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite this, how to activate the capacity of MLLM as the implicit knowledge engine has not been explored yet. Therefore, we propose GeReA, a generate-reason framework that prompts a MLLM like InstructBLIP with question relevant vision and language information to generate knowledge-relevant descriptions and reasons those descriptions for knowledge-based VQA. |
ZIYU MA et. al. | arxiv-cs.CV | 2024-02-04 |
270 | SemPool: Simple, Robust, and Interpretable KG Pooling for Enhancing Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, GNN-based methods for QA rely on the graph information of the candidate answer nodes, which limits their effectiveness in more challenging settings where critical answer information is not included in the KG. We propose a simple graph pooling approach that learns useful semantics of the KG that can aid the LM’s reasoning and that its effectiveness is robust under graph perturbations. |
Costas Mavromatis; Petros Karypis; George Karypis; | arxiv-cs.CL | 2024-02-03 |
271 | CABINET: Content Relevance Based Noise Reduction for Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The irrelevant parts act as noise and are distracting information, resulting in sub-optimal performance due to the vulnerability of LLMs to noise. To mitigate this, we propose CABINET (Content RelevAnce-Based NoIse ReductioN for TablE QuesTion-Answering) – a framework to enable LLMs to focus on relevant tabular data by suppressing extraneous information. |
SOHAN PATNAIK et. al. | arxiv-cs.CL | 2024-02-02 |
272 | Beyond The Answers: Reviewing The Rationality of Multiple Choice Question Answering for The Evaluation of Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study investigates the rationality of MCQA as an evaluation method for LLMs. |
Haochun Wang; Sendong Zhao; Zewen Qiang; Bing Qin; Ting Liu; | arxiv-cs.CL | 2024-02-02 |
273 | SPARQL Generation with Entity Pre-trained GPT for KG Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We managed to isolate which property of the task can be the most difficult to solve at few or zero-shot and we proposed pre-training on all entities (under CWA) to improve the performance. |
Diego Bustamante; Hideaki Takeda; | arxiv-cs.CL | 2024-02-01 |
274 | Proximity QA: Unleashing The Power of Multi-Modal Large Language Models for Spatial Proximity Analysis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, while existing MLLMs adeptly recognize \textit{what} objects are in an image, they still face challenges in effectively discerning \textit{where} these objects are, particularly along the distance (scene depth) axis. To overcome this limitation in MLLMs, we introduce Proximity Question Answering (Proximity QA), a novel framework designed to enable MLLMs to infer the proximity relationship between objects in images. |
Jianing Li; Xi Nan; Ming Lu; Li Du; Shanghang Zhang; | arxiv-cs.CV | 2024-01-31 |
275 | Desiderata for The Context Use of Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most prior work focus on one or two of those problems in isolation, which makes it difficult to see trends across them. We aim to close this gap, by first outlining a set of — previously discussed as well as novel — desiderata for QA models. |
Sagi Shaier; Lawrence E Hunter; Katharina von der Wense; | arxiv-cs.CL | 2024-01-31 |
276 | An Exam-based Evaluation Approach Beyond Traditional Relevance Judgments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose two evaluation measures, the recall-oriented EXAM Cover metric, and the precision-oriented EXAM Qrels metric, the latter which can be implemented with trec_eval. |
Naghmeh Farzi; Laura Dietz; | arxiv-cs.IR | 2024-01-31 |
277 | PipeNet: Question Answering with Semantic Pruning Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we target at finding semantically related entity nodes in the subgraph to improve the efficiency of graph reasoning with KG. |
Ying Su; Jipeng Zhang; Yangqiu Song; Tong Zhang; | arxiv-cs.CL | 2024-01-30 |
278 | Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we provide a Transformer-based model and a baseline benchmark for the Turkish Language. |
Savas Yildirim; | arxiv-cs.CL | 2024-01-30 |
279 | InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work proposes InfoLossQA, a framework to characterize and recover simplification-induced information loss in form of question-and-answer (QA) pairs. |
JAN TRIENES et. al. | arxiv-cs.CL | 2024-01-29 |
280 | Improving Data Augmentation for Robust Visual Question Answering with Effective Curriculum Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Being widely used in learning unbiased visual question answering (VQA) models, Data Augmentation (DA) helps mitigate language biases by generating extra training samples beyond the original samples. |
Yuhang Zheng; Zhen Wang; Long Chen; | arxiv-cs.CV | 2024-01-28 |
281 | LCV2: An Efficient Pretraining-Free Framework for Grounded Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, the LCV2 modular method is proposed for the Grounded Visual Question Answering task in the vision-language multimodal domain. |
Yuhan Chen; Lumei Su; Lihua Chen; Zhiwei Lin; | arxiv-cs.CV | 2024-01-28 |
282 | Augment Before You Try: Knowledge-Enhanced Table Question Answering Via Table Expansion Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a simple yet effective method to integrate external information in a given table. |
YUJIAN LIU et. al. | arxiv-cs.CL | 2024-01-27 |
283 | DataFrame QA: A Universal LLM Framework on DataFrame Question Answering Without Data Exposure Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose DataFrame QA as a comprehensive framework that includes safe Pandas query generation and code execution. |
Junyi Ye; Mengnan Du; Guiling Wang; | arxiv-cs.CL | 2024-01-27 |
284 | A RAG-based Question Answering System Proposal for Understanding Islam: MufassirQAS LLM Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study uses a vector database-based Retrieval Augmented Generation (RAG) approach to enhance the accuracy and transparency of LLMs. |
Ahmet Yusuf Alan; Enis Karaarslan; Ömer Aydin; | arxiv-cs.CL | 2024-01-27 |
285 | Towards Consistent Natural-Language Explanations Via Explanation-Consistency Finetuning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose explanation-consistency finetuning (EC-finetuning), a method that adapts LLMs to generate more consistent natural-language explanations on related examples. |
YANDA CHEN et. al. | arxiv-cs.CL | 2024-01-25 |
286 | Benchmarking Large Language Models in Complex Question Answering Attribution Using Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The current methods for automatically evaluating the attribution, which are often based on Large Language Models (LLMs), are still inadequate, particularly in recognizing subtle differences between attributions, and complex relationships between citations and statements. To compare these attribution evaluation methods and develop new ones, we introduce a set of fine-grained categories (i.e., supportive, insufficient, contradictory and irrelevant) for measuring the attribution, and develop a Complex Attributed Question Answering (CAQA) benchmark by leveraging knowledge graphs (KGs) for automatically generating attributions of different categories to question-answer pairs. |
NAN HU et. al. | arxiv-cs.CL | 2024-01-25 |
287 | Graph Guided Question Answer Generation for Procedural Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on task-specific question answering (QA). |
HAI X. PHAM et. al. | arxiv-cs.CL | 2024-01-24 |
288 | SEER: Facilitating Structured Reasoning and Explanation Via Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose SEER, a novel method that maximizes a structure-based return to facilitate structured reasoning and explanation. |
GUOXIN CHEN et. al. | arxiv-cs.CL | 2024-01-24 |
289 | Can AI Assistants Know What They Don’t Know? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We believe that an AI assistant’s refusal to answer questions it does not know is a crucial method for reducing hallucinations and making the assistant truthful. Therefore, in this paper, we ask the question Can AI assistants know what they don’t know and express them through natural language? |
QINYUAN CHENG et. al. | arxiv-cs.CL | 2024-01-24 |
290 | SpeechDPR: End-to-End Spoken Passage Retrieval for Open-Domain Spoken Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes the first known end-to-end framework, Speech Dense Passage Retriever (SpeechDPR), for the retrieval component of the openSQA problem. |
CHYI-JIUNN LIN et. al. | arxiv-cs.CL | 2024-01-24 |
291 | TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present TROVE, a training-free method of inducing a verifiable and efficient toolbox of functions, by generating via using, growing, and periodically trimming the toolbox. |
Zhiruo Wang; Daniel Fried; Graham Neubig; | arxiv-cs.AI | 2024-01-23 |
292 | Revolutionizing Retrieval-Augmented Generation with Enhanced PDF Structure Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Presently, major foundation model companies have opened up Embedding and Chat API interfaces, and frameworks like LangChain have already integrated the RAG process. |
Demiao Lin; | arxiv-cs.AI | 2024-01-23 |
293 | TAT-LLM: A Specialized Language Model for Discrete Reasoning Over Tabular and Textual Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we address question answering (QA) over a hybrid of tabular and textual data that are very common content on the Web (e.g. SEC filings), where discrete reasoning capabilities are often required. |
FENGBIN ZHU et. al. | arxiv-cs.CL | 2024-01-23 |
294 | CFMatch: Aligning Automated Answer Equivalence Evaluation with Expert Judgments For Open-Domain Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Question answering (QA) can only make progress if we know if an answer is correct, but for many of the most challenging and interesting QA examples, current evaluation metrics to … |
Zongxia Li; Ishani Mondal; Yijun Liang; Huy Nghiem; Jordan Boyd-Graber; | arxiv-cs.CL | 2024-01-23 |
295 | Free Form Medical Visual Question Answering in Radiology Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We innovatively augment the SLAKE dataset, enabling our model to respond to a more diverse array of questions, not limited to the immediate content of radiology or pathology images. |
ABHISHEK NARAYANAN et. al. | arxiv-cs.CV | 2024-01-23 |
296 | Prompt-RAG: Pioneering Vector Embedding-Free Retrieval-Augmented Generation in Niche Domains, Exemplified By Korean Medicine Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a natural language prompt-based retrieval augmented generation (Prompt-RAG), a novel approach to enhance the performance of generative large language models (LLMs) in niche domains. |
Bongsu Kang; Jundong Kim; Tae-Rim Yun; Chang-Eop Kim; | arxiv-cs.CL | 2024-01-20 |
297 | FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the limited data resources and reduce the annotation cost, we introduce FinLLMs, a method for generating financial question-answering data based on common financial formulas using Large Language Models. |
ZIQIANG YUAN et. al. | arxiv-cs.AI | 2024-01-19 |
298 | The Radiation Oncology NLP Database Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The evaluation results in this study could serve as baseline results for future research. |
ZHENGLIANG LIU et. al. | arxiv-cs.CL | 2024-01-19 |
299 | Weakly Supervised Gaussian Contrastive Grounding with Large Multimodal Models for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, there are no human annotations for question-critical timestamps in existing VideoQA datasets. In light of this, we propose a novel weakly supervised framework to enforce the LMMs to reason out the answers with question-critical moments as visual inputs. |
Haibo Wang; Chenghang Lai; Yixuan Sun; Weifeng Ge; | arxiv-cs.CV | 2024-01-19 |
300 | Reinforcement Learning for Question Answering in Programming Domain Using Public Community Scoring As A Human Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we investigate the enhancement of the GPT Neo 125M performance in Community Question Answering (CQA) with a focus on programming, through the integration of Reinforcement Learning from Human Feedback (RLHF) and the utilization of scores from Stack Overflow. |
Alexey Gorbatovski; Sergey Kovalchuk; | arxiv-cs.CL | 2024-01-19 |
301 | Q&A Prompts: Discovering Rich Visual Clues Through Mining Question-Answer Prompts for VQA Requiring Diverse World Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we believe that if we can collect visual clues in the given image as much as possible, we will recognize the image more accurately, understand the question better, recall relevant knowledge more easily, and finally reason out the answer. |
Haibi Wang; Weifeng Ge; | arxiv-cs.CV | 2024-01-19 |
302 | Instant Answering in E-Commerce Buyer-Seller Messaging Using Message-to-Question Reformulation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We seek to automate buyer inquiries to sellers in a leading e-commerce store using a domain-specific federated Question Answering (QA) system. |
BESNIK FETAHU et. al. | arxiv-cs.CL | 2024-01-18 |
303 | Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel Question-Answer Cross-Language-Image Matching framework for WSSS (QA-CLIMS), leveraging the vision-language foundation model to maximize the text-based understanding of images and guide the generation of activation maps. |
Songhe Deng; Wei Zhuo; Jinheng Xie; Linlin Shen; | arxiv-cs.CV | 2024-01-18 |
304 | ChatQA: Surpassing GPT-4 on Conversational QA and RAG Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce ChatQA, a suite of models that outperform GPT-4 on retrieval-augmented generation (RAG) and conversational question answering (QA). |
ZIHAN LIU et. al. | arxiv-cs.CL | 2024-01-18 |
305 | Veagle: Advancements in Multimodal Representation Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Lately, researchers in artificial intelligence have been really interested in how language and vision come together, giving rise to the development of multimodal models that aim … |
RAJAT CHAWLA et. al. | ArXiv | 2024-01-18 |
306 | Fine-tuning Strategies for Domain Specific Question Answering Under Low Annotation Budget Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The unsupervised training of a language model combined with further target task fine-tuning has become the standard QA fine-tuning procedure. In this work, we demonstrate that this strategy is sub-optimal for fine-tuning QA models, especially under a low QA annotation budget, which is a usual setting in practice due to the extractive QA labeling cost. |
Kunpeng Guo; Dennis Diefenbach; Antoine Gourru; Christophe Gravier; | arxiv-cs.CL | 2024-01-17 |
307 | QAnswer: Towards Question Answering Search Over Websites Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To illustrate the potential of QA technologies for the website search practitioner, we demonstrate web searches that combine QA over knowledge graphs and QA over free text — each being usually tackled separately. |
Kunpeng Guo; Clement Defretiere; Dennis Diefenbach; Christophe Gravier; Antoine Gourru; | arxiv-cs.CL | 2024-01-17 |
308 | BERTologyNavigator: Advanced Question Answering with BERT-based Semantics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we introduce the BERTologyNavigator — a two-phased system that combines relation extraction techniques and BERT embeddings to navigate the relationships within the DBLP Knowledge Graph (KG). |
Shreya Rajpal; Ricardo Usbeck; | arxiv-cs.CL | 2024-01-17 |
309 | MMToM-QA: Multimodal Theory of Mind Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Theory of Mind (ToM), the ability to understand people’s minds, is an essential ingredient for developing machines with human-level social intelligence. Recent machine learning … |
CHUANYANG JIN et. al. | ArXiv | 2024-01-16 |
310 | A Study on Large Language Models’ Limitations in Multiple-Choice Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we tackle one of the most widely used tasks – answering Multiple Choice Question (MCQ). |
Aisha Khatun; Daniel G. Brown; | arxiv-cs.CL | 2024-01-15 |
311 | Developing ChatGPT for Biology and Medicine: A Complete Review of Biomedical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper highlights the structures and advancements of medical domain explorations against general domain methods, emphasizing their applications across different tasks and datasets. |
Qing Li; Lei Li; Yu Li; | arxiv-cs.CL | 2024-01-15 |
312 | Uncovering The Full Potential of Visual Grounding Methods in VQA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we demonstrate that current evaluation schemes for VG-methods are problematic due to the flawed assumption of availability of relevant visual information. |
Daniel Reich; Tanja Schultz; | arxiv-cs.CV | 2024-01-15 |
313 | Generalizing Visual Question Answering from Synthetic to Human-Written Questions Via A Chain of QA with A Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, VQA models trained on those data do not perform well on complex, human-written questions. To address this issue, we propose a new method called {\it chain of QA for human-written questions} (CoQAH). |
Taehee Kim; Yeongjae Cho; Heejun Shin; Yohan Jo; Dongmyung Shin; | arxiv-cs.CL | 2024-01-12 |
314 | BOK-VQA: Bilingual Outside Knowledge-Based Visual Question Answering Via Graph Representation Pretraining Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Accordingly, we propose a bilingual outside-knowledge VQA (BOK-VQA) dataset in this study that can be extended to multilingualism. |
Minjun Kim; Seungwoo Song; Youhan Lee; Haneol Jang; Kyungtae Lim; | arxiv-cs.CL | 2024-01-12 |
315 | Probing Structured Semantics Understanding and Generation of Language Models Via Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we examine the ability of LLMs to deal with structured semantics on the tasks of question answering with the help of the human-constructed formal language. |
JINXIN LIU et. al. | arxiv-cs.CL | 2024-01-11 |
316 | Hallucination Benchmark in Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The study provides an in-depth analysis of current models’ limitations and reveals the effectiveness of various prompting strategies. |
Jinge Wu; Yunsoo Kim; Honghan Wu; | arxiv-cs.CL | 2024-01-11 |
317 | Cross-modal Retrieval for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Named entities have diverse visual representations and are therefore difficult to recognize. We argue that cross-modal retrieval may help bridge the semantic gap between an entity and its depictions, and is foremost complementary with mono-modal retrieval. |
Paul Lerner; Olivier Ferret; Camille Guinaudeau; | arxiv-cs.CL | 2024-01-11 |
318 | Answer Retrieval in Legal Community Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Two main challenges hinder applying existing answer retrieval approaches in other domains to the legal domain: (1) a huge knowledge gap between lawyers and non-professionals; and (2) a mix of informal and formal content on legal QA websites. To tackle these challenges, we propose CE_FS, a novel cross-encoder (CE) re-ranker based on the fine-grained structured inputs. |
Arian Askari; Zihui Yang; Zhaochun Ren; Suzan Verberne; | arxiv-cs.IR | 2024-01-09 |
319 | Narrowing The Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose GRANOLA QA, a novel evaluation setting where a predicted answer is evaluated in terms of accuracy and informativeness against a set of multi-granularity answers. |
Gal Yona; Roee Aharoni; Mor Geva; | arxiv-cs.CL | 2024-01-09 |
320 | STAIR: Spatial-Temporal Reasoning with Auditable Intermediate Results for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Though neural module networks are already widely studied on image-text tasks, applying them to videos is a non-trivial task, as reasoning on videos requires different abilities. In this paper, we define a set of basic video-text sub-tasks for video question answering and design a set of lightweight modules to complete them. |
Yueqian Wang; Yuxuan Wang; Kai Chen; Dongyan Zhao; | arxiv-cs.CV | 2024-01-08 |
321 | Building Efficient and Effective OpenQA Systems for Low-Resource Languages Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we show that effective, low-cost OpenQA systems can be developed for low-resource languages. |
EMRAH BUDUR et. al. | arxiv-cs.CL | 2024-01-07 |
322 | A Joint-Reasoning Based Disease Q&A System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Extant QA systems also have limitations in terms of automation and performance. We address these challenges by designing a novel, automated disease QA system which effectively utilizes both LM and KG techniques through a joint-reasoning approach to answer disease-related questions appropriate for lay users. |
Prakash Chandra Sukhwal; Vaibhav Rajan; Atreyi Kankanhalli; | arxiv-cs.CL | 2024-01-06 |
323 | DocGraphLM: Documental Graph Language Model for Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce DocGraphLM, a novel framework that combines pre-trained language models with graph semantics. |
Dongsheng Wang; Zhiqiang Ma; Armineh Nourbakhsh; Kang Gu; Sameena Shah; | arxiv-cs.CL | 2024-01-05 |
324 | Location Aware Modular Biencoder for Tourism Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The traditional method of encoding each pair of question and POI becomes inefficient when the number of candidates increases, making it infeasible for real-world applications. To overcome this, we propose treating the QA task as a dense vector retrieval problem, where we encode questions and POIs separately and retrieve the most relevant POIs for a question by utilizing embedding space similarity. |
Haonan Li; Martin Tomko; Timothy Baldwin; | arxiv-cs.CL | 2024-01-04 |
325 | Joint Multi-Facts Reasoning Network For Complex Temporal Question Answering Over Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose \textbf{\underline{J}}oint \textbf{\underline{M}}ulti \textbf{\underline{F}}acts \textbf{\underline{R}}easoning \textbf{\underline{N}}etwork (JMFRN), to jointly reasoning multiple temporal facts for accurately answering \emph{complex} temporal questions. |
RIKUI HUANG et. al. | arxiv-cs.CL | 2024-01-04 |
326 | Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Through a series of experiments on an extensive benchmark dataset, we compare models of varying sizes with different prompting techniques and identify common issue types in the generated output. |
Phillip Schneider; Manuel Klettner; Kristiina Jokinen; Elena Simperl; Florian Matthes; | arxiv-cs.CL | 2024-01-03 |
327 | Navigating Uncertainty: Optimizing API Dependency for Hallucination Reduction in Closed-Book Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new LLM able to self-estimate if it is able to answer directly or needs to request an external tool. |
Pierre Erbacher; Louis Falissar; Vincent Guigue; Laure Soulier; | arxiv-cs.CL | 2024-01-03 |
328 | Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. |
HAOPENG LI et. al. | arxiv-cs.CV | 2024-01-02 |
329 | Answering from Sure to Uncertain: Uncertainty-Aware Curriculum Learning for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recognizing that conventional self-paced CL methods rely on training loss for difficulty measurement, which might not accurately reflect the intricacies of video-question pairs, we introduce the concept of uncertainty-aware CL. |
Haopeng Li; Qiuhong Ke; Mingming Gong; Tom Drummond; | arxiv-cs.CV | 2024-01-02 |
330 | Glance and Focus: Memory Prompting for Multi-Event Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In contrast, humans can easily tackle it by using a series of episode memories as anchors to quickly locate question-related key moments for reasoning. To mimic this effective reasoning strategy, we propose the Glance-Focus model. |
Ziyi Bai; Ruiping Wang; Xilin Chen; | arxiv-cs.CV | 2024-01-02 |
331 | Question-Answering Based Summarization of Electronic Health Records Using Retrieval Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, the requirement to consider the entire content of an EHR in summarization has resulted in poor performance due to the fact that attention mechanisms in modern large language models (LLMs) adds a quadratic complexity in terms of the size of the input. We propose here a method that mitigates these shortcomings by combining semantic search, retrieval augmented generation (RAG) and question-answering using the latest LLMs. |
Walid Saba; Suzanne Wendelken; James. Shanahan; | arxiv-cs.CL | 2024-01-02 |
332 | Resolving Zero-Shot and Fact-Based Visual Question Answering Via Enhanced Fact Retrieval Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Practical applications with visual question answering (VQA) systems are challenging, and recent research has aimed at investigating this important field. Many issues related to … |
Sen Wu; Guoshuai Zhao; Xueming Qian; | IEEE Transactions on Multimedia | 2024-01-01 |
333 | Test-Time Model Adaptation for Visual Question Answering With Debiased Self-Supervisions Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Visual question answering (VQA) is a prevalent task in real-world, and plays an essential role in helping the blind understand the physical world. However, due to the real-world … |
Z. WEN et. al. | IEEE Transactions on Multimedia | 2024-01-01 |
334 | LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces an alternative to SFT called Natural Language Feedback for Finetuning LLMs (LaFFi). |
QIANXI LI et. al. | arxiv-cs.LG | 2023-12-31 |
335 | Keqing: Knowledge-based Question Answering Is A Nature Chain-of-thought Mentor of LLM Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel framework to assist LLMs, such as ChatGPT, to retrieve question-related structured information on the knowledge graph, and demonstrate that Knowledge-based question answering (Keqing) could be a nature Chain-of-Thought (CoT) mentor to guide the LLM to sequentially find the answer entities of a complex question through interpretable logical chains. |
CHAOJIE WANG et. al. | arxiv-cs.CL | 2023-12-31 |
336 | ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering Over Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the effectiveness, due to the divergence in model architecture, the PLM and GNN are not closely integrated, limiting the knowledge sharing and fine-grained feature interactions. To solve it, we aim to simplify the above two-module approach, and develop a more capable PLM that can directly support subgraph reasoning for KGQA, namely ReasoningLM. |
Jinhao Jiang; Kun Zhou; Wayne Xin Zhao; Yaliang Li; Ji-Rong Wen; | arxiv-cs.CL | 2023-12-30 |
337 | Mitigating The Impact of False Negatives in Dense Retrieval with Contrastive Confidence Regularization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This is because hard negatives are those closer to a given query, and thus more likely to be false negatives. To address this issue, we propose a novel contrastive confidence regularizer for Noise Contrastive Estimation (NCE) loss, a commonly used loss for dense retrieval. |
Shiqi Wang; Yeqin Zhang; Cam-Tu Nguyen; | arxiv-cs.CL | 2023-12-30 |
338 | FusionMind — Improving Question and Answering with External Context Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Answering questions using pre-trained language models (LMs) and knowledge graphs (KGs) presents challenges in identifying relevant knowledge and performing joint reasoning.We compared LMs (fine-tuned for the task) with the previously published QAGNN method for the Question-answering (QA) objective and further measured the impact of additional factual context on the QAGNN performance. |
Shreyas Verma; Manoj Parmar; Palash Choudhary; Sanchita Porwal; | arxiv-cs.CL | 2023-12-30 |
339 | A Simple LLM Framework for Long-Range Video Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present LLoVi, a language-based framework for long-range video question-answering (LVQA). |
CE ZHANG et. al. | arxiv-cs.CV | 2023-12-28 |
340 | AQUALLM: Audio Question Answering Data Generation Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a scalable AQA data generation pipeline, denoted as the AQUALLM framework, which relies on Large Language Models (LLMs). |
Swarup Ranjan Behera; Krishna Mohan Injeti; Jaya Sai Kiran Patibandla; Praveen Kumar Pokala; Balakrishna Reddy Pailla; | arxiv-cs.CL | 2023-12-28 |
341 | S2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, while numerous single-turn datasets are available, we have not utilized them effectively. To solve this problem, we propose a novel method to convert single-turn datasets to multi-turn datasets. |
BAOKUI LI et. al. | arxiv-cs.CL | 2023-12-27 |
342 | From Text to Multimodal: A Comprehensive Survey of Adversarial Example Generation in Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This article aims to comprehensively review adversarial example-generation techniques in the QA field, including textual and multimodal contexts. |
Gulsum Yigit; Mehmet Fatih Amasyali; | arxiv-cs.CL | 2023-12-26 |
343 | Conversational Question Answering with Reformulations Over Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These inputs are easy for human beings to understand given a conversation history, but hard for a machine to interpret, which can degrade ConvQA performance. To address this problem, we propose a reinforcement learning (RL) based model, CornNet, which utilizes question reformulations generated by large language models (LLMs) to improve ConvQA performance. |
Lihui Liu; Blaine Hill; Boxin Du; Fei Wang; Hanghang Tong; | arxiv-cs.CL | 2023-12-26 |
344 | Detection-based Intermediate Supervision for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For instance, (1) a prior assumption that each instance-module refers to only one grounded object yet overlooks other potentially associated grounded objects, impeding full cross-modal alignment learning; (2) IoU-based intermediate supervisions may introduce noise signals as the bounding box overlap issue might guide the model’s focus towards irrelevant objects. To address these issues, a novel method, \textbf{\underline{D}}etection-based \textbf{\underline{I}}ntermediate \textbf{\underline{S}}upervision (DIS), is proposed, which adopts a generative detection framework to facilitate multiple grounding supervisions via sequence generation. |
YUHANG LIU et. al. | arxiv-cs.CV | 2023-12-26 |
345 | KnowledgeNavigator: Leveraging Large Language Models for Enhanced Reasoning Over Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Especially in scenarios that require long logical chains or complex reasoning, the hallucination and knowledge limitation of LLM limit its performance in question answering (QA). In this paper, we propose a novel framework KnowledgeNavigator to address these challenges by efficiently and accurately retrieving external knowledge from knowledge graph and using it as a key factor to enhance LLM reasoning. |
TIEZHENG GUO et. al. | arxiv-cs.CL | 2023-12-25 |
346 | On The Promises and Challenges of Multimodal Foundation Models for Geographical, Environmental, Agricultural, and Urban Planning Applications Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The advent of large language models (LLMs) has heightened interest in their potential for multimodal applications that integrate language and vision. This paper explores the … |
CHENJIAO TAN et. al. | ArXiv | 2023-12-23 |
347 | PokeMQA: Programmable Knowledge Editing for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We thus propose a framework, Programmable knowledge editing for Multi-hop Question Answering (PokeMQA), to decouple the jobs. |
HENGRUI GU et. al. | arxiv-cs.CL | 2023-12-23 |
348 | SEER : A Knapsack Approach to Exemplar Selection for In-Context HybridQA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present Selection of ExEmplars for hybrid Reasoning (SEER), a novel method for selecting a set of exemplars that is both representative and diverse. |
Jonathan Tonglet; Manon Reusens; Philipp Borchert; Bart Baesens; | emnlp | 2023-12-22 |
349 | ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering Over Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the effectiveness, due to the divergence in model architecture, the PLM and GNN are not closely integrated, limiting the knowledge sharing and fine-grained feature interactions. To solve it, we aim to simplify the above two-module approach, and develop a more capable PLM that can directly support subgraph reasoning for KGQA, namely ReasoningLM. |
Jinhao Jiang; Kun Zhou; Xin Zhao; Yaliang Li; Ji-Rong Wen; | emnlp | 2023-12-22 |
350 | Diversity Enhanced Narrative Question Generation for Storybooks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a multi-question generation model (mQG), which is capable of generating multiple, diverse, and answerable questions by focusing on context and questions. |
Hokeun Yoon; JinYeong Bak; | emnlp | 2023-12-22 |
351 | FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability Through 5W Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite progress in automatic text-based fact verification (e. g. , FEVER, LIAR), the research community lacks substantial effort in multimodal fact verification. To address this gap, we introduce FACTIFY 3M, a dataset of 3 million samples that pushes the boundaries of the domain of fact verification via a multimodal fake news dataset, in addition to offering explainability through the concept of 5W question-answering. |
MEGHA CHAKRABORTY et. al. | emnlp | 2023-12-22 |
352 | Beware of Model Collapse! Fast and Stable Test-time Adaptation for Robust Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we delve into why TTA causes model collapse and find that the imbalanced label distribution inherent in QA is the reason for it. |
Yi Su; Yixin Ji; Juntao Li; Hai Ye; Min Zhang; | emnlp | 2023-12-22 |
353 | CRT-QA: A Dataset of Complex Reasoning Question Answering Over Tabular Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we first establish a comprehensive taxonomy of reasoning and operation types for tabular data analysis. Then, we construct a complex reasoning QA dataset over tabular data, named CRT-QA dataset (Complex Reasoning QA over Tabular data), with the following unique features: (1) it is the first Table QA dataset with multi-step operation and informal reasoning; (2) it contains fine-grained annotations on questions? directness, composition types of sub-questions, and human reasoning paths which can be used to conduct a thorough investigation on LLMs? reasoning ability; (3) it contains a collection of unanswerable and indeterminate questions that commonly arise in real-world situations. |
Zhehao Zhang; Xitao Li; Yan Gao; Jian-Guang Lou; | emnlp | 2023-12-22 |
354 | TheoremQA: A Theorem-driven Question Answering Dataset IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce TheoremQA, the first theorem-driven question-answering dataset designed to evaluate AI models� capabilities to apply theorems to solve challenging science problems. |
WENHU CHEN et. al. | emnlp | 2023-12-22 |
355 | Towards A Unified Multimodal Reasoning Framework Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our experiments aimed to fill the gap in current research by investigating the combined impact of CoT and VQA, contributing to the understanding of how these techniques can improve the reasoning capabilities of state-of-the-art models like GPT-4. Results from our experiments demonstrated the potential of these approaches in enhancing LM’s reasoning and question-answering capabilities, providing insights for further research and development in the field, and paving the way for more accurate and reliable AI systems that can handle complex reasoning tasks across multiple modalities. |
Abhinav Arun; Dipendra Singh Mal; Mehul Soni; Tomohiro Sawada; | arxiv-cs.CL | 2023-12-22 |
356 | Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the original dialog inpainting model is trained solely on the dialog reconstruction task, resulting in the generation of questions with low contextual relevance due to insufficient learning of question-answer alignment. To overcome this limitation, we propose a novel framework called Dialogizer, which has the capability to automatically generate ConvQA datasets with high contextual relevance from textual sources. |
YERIN HWANG et. al. | emnlp | 2023-12-22 |
357 | PreWoMe: Exploiting Presuppositions As Working Memory for Long Form Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose PreWoMe, a unified approach capable of handling any type of information-seeking question. |
Wookje Han; Jinsol Park; Kyungjae Lee; | emnlp | 2023-12-22 |
358 | Question Answering As Programming for Solving Time-Sensitive Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This can be attributed to the LLMs� inability to perform rigorous reasoning based on surface-level text semantics. To overcome this limitation, rather than requiring LLMs to directly answer the question, we propose a novel approach where we reframe the Question Answering task as Programming (QAaP). |
XINYU ZHU et. al. | emnlp | 2023-12-22 |
359 | A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we use language models to rewrite snippets from scientific documents to be read on their own. |
Benjamin Newman; Luca Soldaini; Raymond Fok; Arman Cohan; Kyle Lo; | emnlp | 2023-12-22 |
360 | A Simple Baseline for Knowledge-Based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our main contribution in this paper is to propose a much simpler and readily reproducible pipeline which, in a nutshell, is based on efficient in-context learning by prompting LLaMA (1 and 2) using question-informative captions as contextual information. |
Alexandros Xenos; Themos Stafylakis; Ioannis Patras; Georgios Tzimiropoulos; | emnlp | 2023-12-22 |
361 | TempTabQA: Temporal Question Answering for Semi-Structured Tables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Can current NLP systems reason about such information in semi-structured tables? To tackle this question, we introduce the task of temporal question answering on semi-structured tables. |
VIVEK GUPTA et. al. | emnlp | 2023-12-22 |
362 | Text Fact Transfer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Text style transfer is a prominent task that aims to control the style of text without inherently changing its factual content. To cover more text modification applications, such as adapting past news for current events and repurposing educational materials, we propose the task of text fact transfer, which seeks to transfer the factual content of a source text between topics without modifying its style. |
Nishant Balepur; Jie Huang; Kevin Chang; | emnlp | 2023-12-22 |
363 | Language Models with Rationality Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This lack of interpretability is a growing impediment to widespread use of LLMs. To address this, our goals are to make model beliefs and their inferential relationships explicit, and to resolve inconsistencies that may exist, so that answers are supported by interpretable chains of reasoning drawn from a consistent network of beliefs. |
NORA KASSNER et. al. | emnlp | 2023-12-22 |
364 | PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering Via Pluggable Reward-Driven Contextual Adapter Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Incorporating Large Language Models (LLMs) as generators is beneficial due to their advanced QA capabilities, but they are typically too large to be fine-tuned with budget constraints while some of them are only accessible via APIs. To tackle this issue and further improve ReQA performance, we propose a trainable Pluggable Reward-Driven Contextual Adapter (PRCA), keeping the generator as a black box. |
HAOYAN YANG et. al. | emnlp | 2023-12-22 |
365 | ToolWriter: Question Specific Tool Synthesis for Tabular Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unlike humans who use programmatic tools like filters to transform data before processing, language models in TQA process tables directly, resulting in information loss as table size increases. In this paper we propose ToolWriter to generate query specific programs and detect when to apply them to transform tables and align them with the TQA model�s capabilities. |
Carlos Gemmell; Jeff Dalton; | emnlp | 2023-12-22 |
366 | Hop, Union, Generate: Explainable Multi-hop Reasoning Without Rationale Supervision Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work proposes a principled, probabilistic approach for training explainable multi-hop QA systems without rationale supervision. |
Wenting Zhao; Justin Chiu; Claire Cardie; Alexander Rush; | emnlp | 2023-12-22 |
367 | CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose CarExpert, an in-car retrieval-augmented conversational question-answering system leveraging LLMs for different tasks. |
MD RASHAD AL HASAN RONY et. al. | emnlp | 2023-12-22 |
368 | Answering Questions By Meta-Reasoning Over Multiple Chains of Thought IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce Multi-Chain Reasoning (MCR), an approach which prompts large language models to meta-reason over multiple chains of thought, rather than aggregate their answers. |
ORI YORAN et. al. | emnlp | 2023-12-22 |
369 | MarkQA: A Large Scale KBQA Dataset with Numerical Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we focus on the complex numerical reasoning in KBQA, and propose a new task, NR-KBQA, which necessitates the ability to perform both multi-hop reasoning and numerical reasoning. |
Xiang Huang; Sitao Cheng; Yuheng Bao; Shanshan Huang; Yuzhong Qu; | emnlp | 2023-12-22 |
370 | API-Assisted Code Generation for Question Answering on Varied Table Structures Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, this paper introduces a unified TableQA framework that: (1) provides a unified representation for structured tables as multi-index Pandas data frames, (2) uses Python as a powerful querying language, and (3) uses few-shot prompting to translate NL questions into Python programs, which are executable on Pandas data frames. |
Yihan Cao; Shuyi Chen; Ryan Liu; Zhiruo Wang; Daniel Fried; | emnlp | 2023-12-22 |
371 | Interview Evaluation: A Novel Approach for Automatic Evaluation of Conversational Question Answering Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel automatic evaluation approach, interview evaluation. |
XIBO LI et. al. | emnlp | 2023-12-22 |
372 | Pre-training Language Models for Comparative Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework to pre-train language models for enhancing their abilities of comparative reasoning over texts. |
Mengxia Yu; Zhihan Zhang; Wenhao Yu; Meng Jiang; | emnlp | 2023-12-22 |
373 | Large Language Models Are Temporal and Causal Reasoners for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we develop LLaMA-VQA by applying Flipped-VQA to LLaMA, and it outperforms both LLMs-based and non-LLMs-based models on five challenging VideoQA benchmarks. |
Dohwan Ko; Ji Lee; Woo-Young Kang; Byungseok Roh; Hyunwoo Kim; | emnlp | 2023-12-22 |
374 | ViGPTQA – State-of-the-Art LLMs for Vietnamese Question Answering: System Overview, Core Models Training, and Evaluations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a practical real-world implementation of a question answering system for Vietnamese, called ViGPTQA, leveraging the power of LLM. |
Minh Thuan Nguyen; Khanh Tung Tran; Nhu Van Nguyen; Xuan-Son Vu; | emnlp | 2023-12-22 |
375 | IfQA: A Dataset for Open-domain Question Answering Under Counterfactual Presuppositions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although counterfactual reasoning is a fundamental aspect of intelligence, the lack of large-scale counterfactual open-domain question-answering (QA) benchmarks makes it difficult to evaluate and improve models on this ability. To address this void, we introduce the first such dataset, named IfQA, where each question is based on a counterfactual presupposition via an �if� clause. |
Wenhao Yu; Meng Jiang; Peter Clark; Ashish Sabharwal; | emnlp | 2023-12-22 |
376 | From Parse-Execute to Parse-Execute-Refine: Improving Semantic Parser for Complex Question Answering Over Knowledge Base Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we propose three components: a parsing stage, an execution stage and a refinement stage, to enhance the ability of complex reasoning. |
Wangzhen Guo; Linyin Luo; Hanjiang Lai; Jian Yin; | emnlp | 2023-12-22 |
377 | Uncertainty Guided Global Memory Improves Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, attention-based token representations lack explicit global contextual information to connect reasoning steps. To address these issues, we propose GEMFormer, a two-stage method that first collects relevant information over the entire document to the memory and then combines it with local context to solve the task. |
Alsu Sagirova; Mikhail Burtsev; | emnlp | 2023-12-22 |
378 | Navigating The Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The increased deployment of LMs for real-world tasks involving knowledge and facts makes it important to understand model epistemology: what LMs think they know, and how their attitudes toward that knowledge are affected by language use in their inputs. Here, we study an aspect of model epistemology: how epistemic markers of certainty, uncertainty, or evidentiality like �I�m sure it�s�, �I think it�s�, or �Wikipedia says it�s� affect models, and whether they contribute to model failures. |
Kaitlyn Zhou; Dan Jurafsky; Tatsunori Hashimoto; | emnlp | 2023-12-22 |
379 | ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose ZEROTOP, a zero-shot task-oriented parsing method that decomposes semantic parsing problem into a set of abstractive and extractive question-answering (QA) problems. |
Dheeraj Mekala; Jason Wolfe; Subhro Roy; | emnlp | 2023-12-22 |
380 | Causal Reasoning Through Two Cognition Layers for Improving Generalization in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, diverse interpretations of the input lead to various modes of answer generation, highlighting the role of causal reasoning between interpreting and answering steps in VQA. Through this lens, we propose Cognitive pathways VQA (CopVQA) improving the multimodal predictions by emphasizing causal reasoning factors. |
Trang Nguyen; Naoaki Okazaki; | emnlp | 2023-12-22 |
381 | Continual Dialogue State Tracking Via Example-Guided Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Motivated by the insight that dialogue state tracking (DST), a crucial component of dialogue systems that estimates the user�s goal as a conversation proceeds, is a simple natural language understanding task, we propose reformulating it as a bundle of granular example-guided question answering tasks to minimize the task shift between services and thus benefit continual learning. |
HYUNDONG CHO et. al. | emnlp | 2023-12-22 |
382 | Too Much of Product Information : Don�t Worry, Let�s Look for Evidence! Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a distantly supervised solution to answer customer questions by using product information. |
Aryan Jain; Jitenkumar Rana; Chetan Aggarwal; | emnlp | 2023-12-22 |
383 | QA-NatVer: Question Answering for Natural Logic-based Fact Verification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose to use question answering to predict natural logic operators, taking advantage of the generalization capabilities of instruction-tuned language models. |
Rami Aly; Marek Strong; Andreas Vlachos; | emnlp | 2023-12-22 |
384 | Large Language Models Are Complex Table Parsers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to incorporate GPT-3. 5 to address such challenges, in which complex tables are reconstructed into tuples and specific prompt designs are employed for dialogues. |
BOWEN ZHAO et. al. | emnlp | 2023-12-22 |
385 | Selectively Answering Ambiguous Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We investigate question answering from this perspective, focusing on answering a subset of questions with a high degree of accuracy, from a set of questions in which many are inherently ambiguous. |
JEREMY COLE et. al. | emnlp | 2023-12-22 |
386 | Mitigating Temporal Misalignment By Discarding Outdated Facts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To mitigate the effects of temporal misalignment, we propose fact duration prediction: the task of predicting how long a given fact will remain true. |
Michael Zhang; Eunsol Choi; | emnlp | 2023-12-22 |
387 | Merging Generated and Retrieved Knowledge for Open-Domain QA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on the intuition that answers supported by both sources are more likely to be correct, we propose COMBO, a Compatibility-Oriented knowledge Merging for Better Open-domain QA framework, to effectively leverage the two sources of information. |
YUNXIANG ZHANG et. al. | emnlp | 2023-12-22 |
388 | Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Visual Word Sense Disambiguation (VWSD) is a novel challenging task with the goal of retrieving an image among a set of candidates, which better represents the meaning of an ambiguous word within a given context. In this paper, we make a substantial step towards unveiling this interesting task by applying a varying set of approaches. |
Anastasia Kritharoula; Maria Lymperaiou; Giorgos Stamou; | emnlp | 2023-12-22 |
389 | Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we provide a benchmark Question Answering (QA) dataset named MSQA, centered around Microsoft products and IT technical problems encountered by customers. |
FANGKAI YANG et. al. | emnlp | 2023-12-22 |
390 | Diversify Question Generation with Retrieval-Augmented Style Transfer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: These methods, however, have not considered the potential of external knowledge for expression diversity. To bridge this gap, we propose RAST, a framework for Retrieval-Augmented Style Transfer, where the objective is to utilize the style of diverse templates for question generation. |
QI GOU et. al. | emnlp | 2023-12-22 |
391 | Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To cope with the challenge, we propose a novel framework, Tree of Clarifications (ToC): It recursively constructs a tree of disambiguations for the AQ�via few-shot prompting leveraging external knowledge�and uses it to generate a long-form answer. |
Gangwoo Kim; Sungdong Kim; Byeongguk Jeon; Joonsuk Park; Jaewoo Kang; | emnlp | 2023-12-22 |
392 | Evaluating and Modeling Attribution for Cross-Lingual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We find that Natural Language Inference models and PaLM 2 fine-tuned on a very small amount of attribution data can accurately detect attribution. With these models, we improve the attribution level of a cross-lingual QA system. |
BENJAMIN MULLER et. al. | emnlp | 2023-12-22 |
393 | Best of Both Worlds: Towards Improving Temporal Knowledge Base Question Answering Via Targeted Fact Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We model the extraction problem as an open-domain question answering task using off-the-shelf language models. |
NITHISH KANNEN et. al. | emnlp | 2023-12-22 |
394 | Does Named Entity Recognition Truly Not Scale Up to Real-world Product Attribute Extraction? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we argue the scalability of the NER-based approach compared to the QA-based approach, since researchers have compared BERT-based QA-based models to only a weak BiLSTM-based NER baseline trained from scratch in terms of only accuracy on datasets designed to evaluate the QA-based approach. |
Wei-Te Chen; Keiji Shinzato; Naoki Yoshinaga; Yandi Xia; | emnlp | 2023-12-22 |
395 | Numerical Reasoning for Financial Reports Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We devised a method to locate critical data, and leverage the FinQA dataset to fine-tune both Llama-2 7B and T5 models for customized question answering. |
Abhinav Arun; Ashish Dhiman; Mehul Soni; Yibei Hu; | arxiv-cs.CL | 2023-12-22 |
396 | GazeVQA: A Video Question Answering Dataset for Multiview Eye-Gaze Task-Oriented Collaborations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we build a novel task-oriented VQA dataset, called GazeVQA, for collaborative tasks where gaze information is captured during the task process. |
MUHAMMET ILASLAN et. al. | emnlp | 2023-12-22 |
397 | Continually Improving Extractive QA Via Human Feedback Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We study continually improving an extractive question answering (QA) system via human user feedback. |
Ge Gao; Hung-Ting Chen; Yoav Artzi; Eunsol Choi; | emnlp | 2023-12-22 |
398 | Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions? IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we introduce InfoSeek, a visual question answering dataset tailored for information-seeking questions that cannot be answered with only common sense knowledge. |
YANG CHEN et. al. | emnlp | 2023-12-22 |
399 | LingoQA: Video Question Answering for Autonomous Driving Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Nonetheless, evaluating the performance of Video QA models has proved particularly tough due to the absence of comprehensive benchmarks. To fill this gap, we introduce LingoQA, a benchmark specifically for autonomous driving Video QA. |
ANA-MARIA MARCU et. al. | arxiv-cs.RO | 2023-12-21 |
400 | DriveLM: Driving with Graph Visual Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We instantiate datasets (DriveLM-Data) built upon nuScenes and CARLA, and propose a VLM-based baseline approach (DriveLM-Agent) for jointly performing Graph VQA and end-to-end driving. |
CHONGHAO SIMA et. al. | arxiv-cs.CV | 2023-12-21 |
401 | Perception Test 2023: A Summary of The First Challenge And Outcome Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We summarise in this report the task descriptions, metrics, baselines, and results. |
Joseph Heyward; João Carreira; Dima Damen; Andrew Zisserman; Viorica Pătrăucean; | arxiv-cs.CV | 2023-12-20 |
402 | Object-aware Adaptive-Positivity Learning for Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose to explicitly consider fine-grained visual objects in video frames (object-level clues) and explore the multi-modal relations(i.e., the object, audio, and question) in terms of feature interaction and model optimization. |
Zhangbin Li; Dan Guo; Jinxing Zhou; Jing Zhang; Meng Wang; | arxiv-cs.CV | 2023-12-20 |
403 | PEPT: Expert Finding Meets Personalized Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these models usually learn pure text representations of experts from histories, disregarding personalized and fine-grained expert modeling. For alleviating this, we present a personalized pre-training and fine-tuning paradigm, which could effectively learn expert interest and expertise simultaneously. |
Qiyao Peng; Hongtao Liu; Hongyan Xu; Yinghui Wang; Wenjun Wang; | arxiv-cs.IR | 2023-12-19 |
404 | Cross-Modal Reasoning with Event Correlation for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the dense caption modality as a new auxiliary and distill event-correlated information from it to infer the correct answer. |
CHENGXIANG YIN et. al. | arxiv-cs.CV | 2023-12-19 |
405 | Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing VQA methods are incapable of handling Knowledge-based Visual Question Answering (KB-VQA), which requires external knowledge beyond visible contents to answer questions about a given image. To address this issue, we propose a novel framework that endows the model with capabilities of answering more general questions, and achieves a better exploitation of external knowledge through generating Multiple Clues for Reasoning with Memory Neural Networks (MCR-MemNN). |
Chengxiang Yin; Zhengping Che; Kun Wu; Zhiyuan Xu; Jian Tang; | arxiv-cs.CV | 2023-12-19 |
406 | VQA4CIR: Boosting Composed Image Retrieval with Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Albeit progress has been made in Composed Image Retrieval (CIR), we empirically find that a certain percentage of failure retrieval results are not consistent with their relative captions. |
CHUN-MEI FENG et. al. | arxiv-cs.CV | 2023-12-19 |
407 | On Early Detection of Hallucinations in Factual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore if the artifacts associated with the model generations can provide hints that the generation will contain hallucinations. |
Ben Snyder; Marius Moisescu; Muhammad Bilal Zafar; | arxiv-cs.CL | 2023-12-19 |
408 | Relation-Aware Question Answering for Heterogeneous Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this way, the interaction between entity and relation is enhanced, and we derive better entity and relation representations. |
HAOWEI DU et. al. | arxiv-cs.CL | 2023-12-19 |
409 | EarthVQA: Towards Queryable Earth Via Relational Reasoning-Based Remote Sensing Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As objects are the basis for complex relational reasoning, we propose a Semantic OBject Awareness framework (SOBA) to advance VQA in an object-centric way. |
Junjue Wang; Zhuo Zheng; Zihang Chen; Ailong Ma; Yanfei Zhong; | arxiv-cs.CV | 2023-12-19 |
410 | Do LLMs Work on Charts? Designing Few-Shot Prompts for Chart Question Answering and Summarization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose PromptChart, a multimodal few-shot prompting framework with LLMs for chart-related applications. |
XUAN LONG DO et. al. | arxiv-cs.CL | 2023-12-17 |
411 | Towards Designing A Question-Answering Chatbot for Online News: Understanding Questions and Perspectives Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By combining results from the studies, we present alignments and discrepancies between how journalists and readers want to use QA chatbots and propose a framework for designing effective QA chatbots in newsrooms. |
Md Naimul Hoque; Ayman Mahfuz; Mayukha Kindi; Naeemul Hassan; | arxiv-cs.HC | 2023-12-17 |
412 | An Evaluation of GPT-4V and Gemini in Online VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We conduct fine-grained analysis by generating seven types of metadata for nearly 2,000 visual questions, such as image type and the required image processing capabilities. |
Mengchen Liu; Chongyan Chen; Danna Gurari; | arxiv-cs.CV | 2023-12-17 |
413 | RJUA-QA: A Comprehensive QA Dataset for Urology Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce RJUA-QA, a novel medical dataset for question answering (QA) and reasoning with clinical evidence, contributing to bridge the gap between general large language models (LLMs) and medical-specific LLM applications. |
SHIWEI LYU et. al. | arxiv-cs.CL | 2023-12-15 |
414 | GSQA: An End-to-End Model for Generative Spoken Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While this extractive-based approach is effective when answers are present directly within the input, it falls short in addressing abstractive questions, where answers are not directly extracted but inferred from the given information. To bridge this gap, we introduce the first end-to-end Generative Spoken Question Answering (GSQA) model that empowers the system to engage in abstractive reasoning. |
MIN-HAN SHIH et. al. | arxiv-cs.CL | 2023-12-15 |
415 | ReST Meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These systems, however, suffer from various failure cases, and we cannot directly train them end-to-end to fix such failures, as interaction with external knowledge is non-differentiable. To address these deficiencies, we define a ReAct-style LLM agent with the ability to reason and act upon external knowledge. |
RENAT AKSITOV et. al. | arxiv-cs.CL | 2023-12-15 |
416 | Advancing Surgical VQA with Scene Graph Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a novel surgical VQA dataset and model and show that results can be significantly improved by incorporating geometric scene features in the VQA model design. |
KUN YUAN et. al. | arxiv-cs.CV | 2023-12-15 |
417 | ViLA: Efficient Video-Language Alignment for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose an efficient Video-Language Alignment (ViLA) network. |
XIJUN WANG et. al. | arxiv-cs.CV | 2023-12-13 |
418 | SocialStigmaQA: A Benchmark to Uncover Stigma Amplification in Generative Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a comprehensive benchmark that is meant to capture the amplification of social bias, via stigmas, in generative language models. |
Manish Nagireddy; Lamogha Chiazor; Moninder Singh; Ioana Baldini; | arxiv-cs.CL | 2023-12-12 |
419 | Vista-LLaMA: Reliable Video Narrator Via Equal Distance to Visual Tokens IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Recent advances in large video-language models have displayed promising outcomes in video comprehension. Current approaches straightforwardly convert video into language tokens … |
FAN MA et. al. | ArXiv | 2023-12-12 |
420 | BESTMVQA: A Benchmark Evaluation System for Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they often suffer from (i) the data insufficient problem, which makes it difficult to train the state of the arts (SOTAs) for the domain-specific task, and (ii) the reproducibility problem, that many existing models have not been thoroughly evaluated in a unified experimental setup. To address these issues, this paper develops a Benchmark Evaluation SysTem for Medical Visual Question Answering, denoted by BESTMVQA. |
Xiaojie Hong; Zixin Song; Liangzhi Li; Xiaoli Wang; Feiyan Liu; | arxiv-cs.AI | 2023-12-12 |
421 | NuScenes-MQA: Integrated Evaluation of Captions and QA for Autonomous Driving Datasets Using Markup Annotations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce Markup-QA, a novel dataset annotation technique in which QAs are enclosed within markups. |
Yuichi Inoue; Yuki Yada; Kotaro Tanahashi; Yu Yamaguchi; | arxiv-cs.CV | 2023-12-11 |
422 | Grounded Question-Answering in Long Egocentric Videos Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we delve into open-ended question-answering (QA) in long, egocentric videos, which allows individuals or robots to inquire about their own past visual experiences. |
Shangzhe Di; Weidi Xie; | arxiv-cs.CV | 2023-12-11 |
423 | Safety Alignment in NLP Tasks: Weakly Aligned Summarization As An In-Context Attack Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our study, focusing on safety-sensitive documents obtained through adversarial attacks, reveals significant disparities in the safety alignment of various NLP tasks. |
Yu Fu; Yufei Li; Wen Xiao; Cong Liu; Yue Dong; | arxiv-cs.CL | 2023-12-11 |
424 | Evaluating ChatGPT As A Question Answering System: A Comprehensive Analysis and Comparison with Existing Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In the current era, a multitude of language models has emerged to cater to user inquiries. Notably, the GPT-3.5 Turbo language model has gained substantial attention as the … |
Hossein Bahak; Farzaneh Taheri; Zahra Zojaji; Arefeh Kazemi; | ArXiv | 2023-12-11 |
425 | PaperQA: Retrieval-Augmented Generative Agent for Scientific Research IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Retrieval-Augmented Generation (RAG) models have been proposed to reduce hallucinations and provide provenance for how an answer was generated. |
JAKUB LÁLA et. al. | arxiv-cs.CL | 2023-12-08 |
426 | DelucionQA: Detecting Hallucinations in Domain-specific Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Detecting hallucinations through automated methods is thus paramount. To facilitate research in this direction, we introduce a sophisticated dataset, DelucionQA, that captures hallucinations made by retrieval-augmented LLMs for a domain-specific QA task. |
MOBASHIR SADAT et. al. | arxiv-cs.CL | 2023-12-08 |
427 | Retrieval-based Video Language Model for Efficient Long Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, the presence of abundant question-irrelevant tokens introduces noise to the video QA process. To address these issues, we introduce a simple yet effective retrieval-based video language model (R-VLM) for efficient and interpretable long video QA. |
Jiaqi Xu; Cuiling Lan; Wenxuan Xie; Xuejin Chen; Yan Lu; | arxiv-cs.CV | 2023-12-08 |
428 | MoVQA: A Benchmark of Versatile Question-Answering for Long-Form Movie Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, their QAs are unduly narrow and modality-biased, lacking a wider view of understanding long-term video content with rich dynamics and complex narratives. To remedy this, we introduce MoVQA, a long-form movie question-answering dataset, and benchmark to assess the diverse cognitive capabilities of multimodal systems rely on multi-level temporal lengths, with considering both video length and clue length. |
HONGJIE ZHANG et. al. | arxiv-cs.CV | 2023-12-07 |
429 | LifelongMemory: Leveraging LLMs for Answering Queries in Long-form Egocentric Videos Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper we introduce LifelongMemory, a new framework for accessing long-form egocentric videographic memory through natural language question answering and retrieval. |
Ying Wang; Yanlai Yang; Mengye Ren; | arxiv-cs.CV | 2023-12-07 |
430 | Language Model Knowledge Distillation for Efficient Question Answering in Spanish Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Therefore, smaller distilled models for the Spanish language could be proven to be highly scalable and facilitate their further adoption on a variety of tasks and scenarios. In this work, we take one step in this direction by developing SpanishTinyRoBERTa, a compressed language model based on RoBERTa for efficient question answering in Spanish. |
Adrián Bazaga; Pietro Liò; Gos Micklem; | arxiv-cs.CL | 2023-12-07 |
431 | PCoQA: Persian Conversational Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In the pursuit of conversational question answering research, we introduce the PCoQA, the first \textbf{P}ersian \textbf{Co}nversational \textbf{Q}uestion \textbf{A}nswering dataset, a resource comprising information-seeking dialogs encompassing a total of 9,026 contextually-driven questions. |
HAMED HEMATIAN HEMATI et. al. | arxiv-cs.CL | 2023-12-07 |
432 | XAIQA: Explainer-Based Data Augmentation for Extractive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel approach, XAIQA, for generating synthetic QA pairs at scale from data naturally available in electronic health records. |
JOEL STREMMEL et. al. | arxiv-cs.CL | 2023-12-06 |
433 | Let The LLMs Talk: Simulating Human-to-Human Conversational QA Via Zero-Shot LLM-to-LLM Interactions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite its effectiveness, challenges exist as human annotation is time-consuming, inconsistent, and not scalable. To address this issue and investigate the applicability of large language models (LLMs) in CQA simulation, we propose a simulation framework that employs zero-shot learner LLMs for simulating teacher-student interactions. |
Zahra Abbasiantaeb; Yifei Yuan; Evangelos Kanoulas; Mohammad Aliannejadi; | arxiv-cs.CL | 2023-12-05 |
434 | PoQuAD – The Polish Question Answering Dataset – Description and Analysis Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This paper showcases PoQuAD — a SQuAD-like contribution to building Question Answering tools for Polish. It largely follows the usual Machine Reading Comprehension format, but a … |
Ryszard Tuora; Aleksandra Zwierzchowska; Natalia Zawadzka-Paluektau; Cezary Klamra; Łukasz Kobyliński; | Proceedings of the 12th Knowledge Capture Conference 2023 | 2023-12-05 |
435 | Unleashing The Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenario Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a zero-shot VQA model named Zero-shot VQA for Flood Disaster Damage Assessment (ZFDDA). |
Yimin Sun; Chao Wang; Yan Peng; | arxiv-cs.CV | 2023-12-04 |
436 | GNN2R: Weakly-Supervised Rationale-Providing Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Second, it is difficult to maintain high efficiency when explicit KG triples need to be retrieved to generate explanations. In this paper, we propose a novel Graph Neural Network-based Two-Step Reasoning model (GNN2R) to solve this issue. |
Ruijie Wang; Luca Rossetto; Michael Cochez; Abraham Bernstein; | arxiv-cs.CL | 2023-12-04 |
437 | How to Configure Good In-Context Sequence for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To enhance the ICL performance, in this study, we use Visual Question Answering (VQA) as case study to explore diverse in-context configurations to find the powerful ones. |
Li Li; Jiawei Peng; Huiyi Chen; Chongyang Gao; Xu Yang; | arxiv-cs.CV | 2023-12-03 |
438 | Harnessing The Power of Prompt-based Techniques for Generating School-Level Questions Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel approach that utilizes prompt-based techniques to generate descriptive and reasoning-based questions. |
Subhankar Maity; Aniket Deroy; Sudeshna Sarkar; | arxiv-cs.CL | 2023-12-02 |
439 | Towards Leveraging LLMs for Conditional QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Utilizing the Conditional Question Answering (CQA) dataset and focusing on generative models like T5 and UL2, we assess the performance of LLMs across diverse question types. |
Syed-Amad Hussain; Parag Pravin Dakle; SaiKrishna Rallabandi; Preethi Raghavan; | arxiv-cs.CL | 2023-12-02 |
440 | KI-MAG: A Knowledge-infused Abstractive Question Answering System in Medical Domain Related Papers Related Patents Related Grants Related Venues Related Experts View |
Aizan Zafar; Sovan Kumar Sahoo; Harsh Bhardawaj; Amitava Das; Asif Ekbal; | Neurocomputing | 2023-12-01 |
441 | BERT and Hierarchical Cross Attention-based Question Answering Over Bridge Inspection Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View |
JIANXI YANG et. al. | Expert Syst. Appl. | 2023-12-01 |
442 | Semantic Parsing for Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel method with graph-to-segment mapping for question answering over knowledge graphs, which helps understanding question utterances. |
Sijia Wei; Wenwen Zhang; Qisong Li; Jiang Zhao; | arxiv-cs.CL | 2023-12-01 |
443 | Zero-Shot Video Question Answering with Procedural Programs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose to answer zero-shot questions about videos by generating short procedural programs that derive a final answer from solving a sequence of visual subtasks. |
Rohan Choudhury; Koichiro Niinuma; Kris M. Kitani; László A. Jeni; | arxiv-cs.CV | 2023-12-01 |
444 | ESG Accountability Made Easy: DocQA at Your Service Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Users can explore over 10,000 Environmental, Social, and Governance (ESG) disclosure reports from over 2000 corporations. |
LOKESH MISHRA et. al. | arxiv-cs.CL | 2023-11-30 |
445 | Enhancing Answer Selection in Community Question Answering with Pre-trained and Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we apply the BERT model as the encoder layer to do pre-training for question subjects, question bodies and answers, respectively, then the cross attention mechanism selects the most relevant answer for different questions. |
Xinghang Hu; | arxiv-cs.CL | 2023-11-29 |
446 | AviationGPT: A Large Language Model for The Aviation Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The emergence of LLMs presents an opportunity to transform this situation, but there is a lack of LLMs specifically designed for the aviation domain. To address this gap, we propose AviationGPT, which is built on open-source LLaMA-2 and Mistral architectures and continuously trained on a wealth of carefully curated aviation datasets. |
Liya Wang; Jason Chou; Xin Zhou; Alex Tien; Diane M Baumgartner; | arxiv-cs.CL | 2023-11-29 |
447 | Towards Top-Down Reasoning: An Explainable Multi-Agent Approach for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This not only facilitates an accurate answer but also provides a transparent rationale for the decision-making pathway. Motivated by this cognitive mechanism, we introduce a novel, explainable multi-agent collaboration framework designed to imitate human-like top-down reasoning by leveraging the expansive knowledge of Large Language Models (LLMs). |
ZEQING WANG et. al. | arxiv-cs.CV | 2023-11-28 |
448 | Fully Authentic Visual Question Answering Dataset from Online Communities Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce the first VQA dataset in which all contents originate from an authentic use case. |
CHONGYAN CHEN et. al. | arxiv-cs.CV | 2023-11-27 |
449 | Characterizing Video Question Answering with Sparsified Inputs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this way, we experiment over public VideoQA benchmarks and provide analysis on how sparsified inputs affect the performance. |
Shiyuan Huang; Robinson Piramuthu; Vicente Ordonez; Shih-Fu Chang; Gunnar A. Sigurdsson; | arxiv-cs.CV | 2023-11-27 |
450 | Releasing The CRaQAn (Coreference Resolution in Question-Answering): An Open-source Dataset and Dataset Creation Methodology Using Instruction-following Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we present our Coreference Resolution in Question-Answering (CRaQAn) dataset, an open-source dataset that caters to the nuanced information retrieval requirements of coreference resolution in question-answering tasks by providing over 250 question-answer pairs containing coreferences. |
ROB GRZYWINSKI et. al. | arxiv-cs.CL | 2023-11-27 |
451 | Uncertainty-aware Language Modeling for Selective Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present an automatic large language model (LLM) conversion approach that produces uncertainty-aware LLMs capable of estimating uncertainty with every prediction. |
QI YANG et. al. | arxiv-cs.CL | 2023-11-26 |
452 | Optimizing and Fine-tuning Large Language Model for Urban Renewal Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study aims to innovatively explore adaptive applications of large language models (LLM) in urban renewal. |
XI WANG et. al. | arxiv-cs.CL | 2023-11-26 |
453 | A Comparative and Experimental Study on Automatic Question Answering Systems and Its Robustness Against Word Jumbling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Question answer generation using Natural Language Processing models is ubiquitous in the world around us. |
Shashidhar Reddy Javaji; Haoran Hu; Sai Sameer Vennam; Vijaya Gajanan Buddhavarapu; | arxiv-cs.CL | 2023-11-26 |
454 | See and Think: Embodied Agent in Virtual Environment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose STEVE, a comprehensive and visionary embodied agent in the Minecraft virtual environment. |
ZHONGHAN ZHAO et. al. | arxiv-cs.AI | 2023-11-26 |
455 | AutoEval-Video: An Automatic Benchmark for Assessing Large Vision Language Models in Open-Ended Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel and challenging benchmark, AutoEval-Video, to comprehensively evaluate large vision-language models in open-ended video question answering. |
Xiuyuan Chen; Yuan Lin; Yuchen Zhang; Weiran Huang; | arxiv-cs.CV | 2023-11-24 |
456 | Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel approach: Probabilistic Tree-of-thought Reasoning (ProbTree). |
SHULIN CAO et. al. | arxiv-cs.CL | 2023-11-23 |
457 | Question Answering in Natural Language: The Special Case of Temporal Expressions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our work aims to leverage a popular approach used for general question answering, answer extraction, in order to find answers to temporal questions within a paragraph. |
Armand Stricker; | arxiv-cs.CL | 2023-11-23 |
458 | Drilling Down Into The Discourse Structure with LLMs for Long Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We aim to assess the applicability of large language models (LLMs) in the task of zero-shot long document evidence retrieval, owing to their unprecedented performance across various NLP tasks. |
Inderjeet Nair; Shwetha Somasundaram; Apoorv Saxena; Koustava Goswami; | arxiv-cs.CL | 2023-11-22 |
459 | FinanceBench: A New Benchmark for Financial Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We test 16 state of the art model configurations (including GPT-4-Turbo, Llama2 and Claude2, with vector stores and long context prompts) on a sample of 150 cases from FinanceBench, and manually review their answers (n=2,400). |
PRANAB ISLAM et. al. | arxiv-cs.CL | 2023-11-20 |
460 | Taiyi: A Bilingual Fine-Tuned Large Language Model for Diverse Biomedical Tasks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To investigate the effectiveness of the fine-tuned LLMs on diverse biomedical NLP tasks in different languages, We present Taiyi, a bilingual fine-tuned LLM for diverse biomedical tasks. |
LING LUO et. al. | arxiv-cs.CL | 2023-11-20 |
461 | PEFT-MedAware: Large Language Model for Medical Awareness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Chat models are capable of answering a wide range of questions, however, the accuracy of their responses is highly uncertain. In this research, we propose a specialized PEFT-MedAware model where we utilize parameter-efficient fine-tuning (PEFT) to enhance the Falcon-1b large language model on specialized MedQuAD data consisting of 16,407 medical QA pairs, leveraging only 0.44% of its trainable parameters to enhance computational efficiency. |
Keivalya Pandya; | arxiv-cs.CL | 2023-11-17 |
462 | FairytaleCQA: Integrating A Commonsense Knowledge Graph Into Children’s Storybook Narratives Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce the FairytaleCQA dataset, which is annotated by children education experts, to supplement 278 storybook narratives with educationally appropriate commonsense knowledge. |
JIAJU CHEN et. al. | arxiv-cs.CL | 2023-11-16 |
463 | Towards Robust Temporal Reasoning of Large Language Models Via A Multi-Hop QA Dataset and Pseudo-Instruction Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a complex temporal question-answering (QA) dataset Complex-TR that focuses on multi-answer and multi-hop temporal reasoning. |
Qingyu Tan; Hwee Tou Ng; Lidong Bing; | arxiv-cs.CL | 2023-11-16 |
464 | Leveraging LLMs in Scholarly Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a scholarly Knowledge Graph Question Answering (KGQA) that answers bibliographic natural language questions by leveraging a large language model (LLM) in a few-shot manner. |
Tilahun Abedissa Taffa; Ricardo Usbeck; | arxiv-cs.CL | 2023-11-16 |
465 | Crafting In-context Examples According to LMs’ Parametric Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We perform analysis on three multi-answer question answering datasets, which allows us to further study answer set ordering strategies based on the LM’s knowledge of each answer. |
Yoonsang Lee; Pranav Atreya; Xi Ye; Eunsol Choi; | arxiv-cs.CL | 2023-11-16 |
466 | Graph-Guided Reasoning for Multi-Hop Question Answering in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address them, we propose a graph-guided CoT prompting method, which guides the LLMs to reach the correct answer with graph representation/verification steps. |
Jinyoung Park; Ameen Patel; Omar Zia Khan; Hyunwoo J. Kim; Joo-Kyung Kim; | arxiv-cs.CL | 2023-11-16 |
467 | On Evaluating The Integration of Reasoning and Action in LLM Agents with Database Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the challenge of accurately assessing answer quality, we introduce a multi-agent evaluation framework that simulates the academic peer-review process, enhancing the precision and reliability of our evaluations. |
LINYONG NAN et. al. | arxiv-cs.CL | 2023-11-16 |
468 | Never Lost in The Middle: Improving Large Language Models Via Attention Strengthening Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The lost in the middle problem challenges most LLMs, referring to the dramatic decline in accuracy when correct information is located in the middle. To overcome this crucial issue, this paper proposes to enhance the information searching and reflection ability of LLMs in long contexts via specially designed tasks called Attention Strengthening Multi-doc QA (ASM QA). |
HE JUNQING et. al. | arxiv-cs.CL | 2023-11-15 |
469 | Improving Zero-shot Visual Question Answering Via Large Language Models with Reasoning Question Prompts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we present Reasoning Question Prompts for VQA tasks, which can further activate the potential of LLMs in zero-shot scenarios. |
YUNSHI LAN et. al. | arxiv-cs.CV | 2023-11-15 |
470 | Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In a high-risk domain such as maternal and infant health, a question-answering system must recognize these pragmatic constraints and go beyond simply answering user questions, examining them in context to respond helpfully. To achieve this, we study assumptions and implications, or pragmatic inferences, made when mothers ask questions about pregnancy and infant care by collecting a dataset of 2,727 inferences from 500 questions across three diverse sources. |
NEHA SRIKANTH et. al. | arxiv-cs.CL | 2023-11-15 |
471 | Long-form Question Answering: An Iterative Planning-Retrieval-Generation Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, generating detailed long-form answers often entails aggregating knowledge from diverse sources. To address these limitations, we propose an LFQA model with iterative Planning, Retrieval, and Generation. |
Pritom Saha Akash; Kashob Kumar Roy; Lucian Popa; Kevin Chen-Chuan Chang; | arxiv-cs.CL | 2023-11-15 |
472 | SQATIN: Supervised Instruction Tuning Meets Question Answering for Improved Dialogue NLU Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce SQATIN, a new framework for dialog NLU based on (i) instruction tuning and (ii) question-answering-based formulation of ID and VE tasks. |
Evgeniia Razumovskaia; Goran Glavaš; Anna Korhonen; Ivan Vulić; | arxiv-cs.CL | 2023-11-15 |
473 | Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce the problem of few-shot transfer learning for KBQA, where the target domain offers only a few labeled examples, but a large labeled training dataset is available in a source domain. |
Mayur Patidar; Riya Sawhney; Avinash Singh; Biswajit Chatterjee; Indrajit Bhattacharya; | arxiv-cs.CL | 2023-11-15 |
474 | LLMRefine: Pinpointing and Refining Large Language Models Via Fine-Grained Actionable Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose LLMRefine, an inference time optimization method to refine LLM’s output. |
WENDA XU et. al. | arxiv-cs.CL | 2023-11-15 |
475 | Temporal Knowledge Question Answering Via Abstract Reasoning Induction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we tackle the significant challenge of temporal knowledge reasoning in Large Language Models (LLMs), an area where such models frequently encounter difficulties. |
Ziyang Chen; Dongfang Li; Xiang Zhao; Baotian Hu; Min Zhang; | arxiv-cs.CL | 2023-11-15 |
476 | Asking More Informative Questions for Grounded Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present an approach that formulates more informative, open-ended questions. |
Sedrick Keh; Justin T. Chiu; Daniel Fried; | arxiv-cs.CL | 2023-11-14 |
477 | Attribute Diversity Determines The Systematicity Gap in VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study the systematicity gap in visual question answering: the performance difference between reasoning on previously seen and unseen combinations of object attributes. |
Ian Berlot-Attwell; A. Michael Carrell; Kumar Krishna Agrawal; Yash Sharma; Naomi Saphra; | arxiv-cs.LG | 2023-11-14 |
478 | XplainLLM: A QA Explanation Dataset for Understanding LLM Decision-Making Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite their remarkable performance, understanding their decision-making process remains a big challenge. In this paper, we look into bringing some transparency to this process by introducing a new explanation dataset for question answering (QA) tasks that integrates knowledge graphs (KGs) in a novel way. |
Zichen Chen; Jianda Chen; Mitali Gaidhani; Ambuj Singh; Misha Sra; | arxiv-cs.CL | 2023-11-14 |
479 | Learning to Filter Context for Retrieval-Augmented Generation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This can cause over- or under-reliance on context, and result in problems in the generated output such as hallucinations. To alleviate these problems, we propose FILCO, a method that improves the quality of the context provided to the generator by (1) identifying useful context based on lexical and information-theoretic approaches, and (2) training context filtering models that can filter retrieved contexts at test time. |
Zhiruo Wang; Jun Araki; Zhengbao Jiang; Md Rizwan Parvez; Graham Neubig; | arxiv-cs.CL | 2023-11-14 |
480 | TempTabQA: Temporal Question Answering for Semi-Structured Tables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Can current NLP systems reason about such information in semi-structured tables? To tackle this question, we introduce the task of temporal question answering on semi-structured tables. |
VIVEK GUPTA et. al. | arxiv-cs.CL | 2023-11-14 |
481 | Understanding Calibration for Multilingual Question Answering Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the calibration properties of several pre-trained multilingual large language models (LLMs) on a variety of question-answering tasks. |
Yahan Yang; Soham Dan; Dan Roth; Insup Lee; | arxiv-cs.CL | 2023-11-14 |
482 | Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most existing prompting methods either rely on one or two of these sources, or require repeatedly invoking large language models to generate similar or identical content. In this work, we overcome these limitations by introducing a novel semi-structured prompting approach that seamlessly integrates the model’s parametric memory with unstructured knowledge from text documents and structured knowledge from knowledge graphs. |
Xin Su; Tiep Le; Steven Bethard; Phillip Howard; | arxiv-cs.CL | 2023-11-14 |
483 | Insights Into Classifying and Mitigating LLMs’ Hallucinations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our research addresses this critical issue within the HeReFaNMi (Health-Related Fake News Mitigation) project, generously supported by NGI Search, dedicated to combating Health-Related Fake News dissemination on the Internet. This endeavour represents a concerted effort to safeguard the integrity of information dissemination in an age of evolving AI technologies. |
Alessandro Bruno; Pier Luigi Mazzeo; Aladine Chetouani; Marouane Tliba; Mohamed Amine Kerkouri; | arxiv-cs.CL | 2023-11-14 |
484 | RECALL: A Benchmark for LLMs Robustness Against External Counterfactual Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our benchmark consists of two tasks, Question Answering and Text Generation, and for each task, we provide models with a context containing counterfactual information. |
YI LIU et. al. | arxiv-cs.CL | 2023-11-14 |
485 | Evaluating LLMs on Document-Based QA: Exact Answer Selection and Numerical Extraction Using Cogtale Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While some existing work focus on evaluating large language models performance on retrieving and answering questions from documents, assessing the LLMs performance on QA types that require exact answer selection from predefined options and numerical extraction is yet to be fully assessed. In this paper, we specifically focus on this underexplored context and conduct empirical analysis of LLMs (GPT-4 and GPT-3.5) on question types, including single-choice, yes-no, multiple-choice, and number extraction questions from documents in zero-shot setting. |
ZAFARYAB RASOOL et. al. | arxiv-cs.IR | 2023-11-13 |
486 | A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Yet, the true challenge lies in the domain of knowledge-intensive VQA tasks, which necessitate not just recognition of visual elements, but also a deep comprehension of the visual information in conjunction with a vast repository of learned knowledge. To uncover such capabilities of MLMs, particularly the newly introduced GPT-4V, we provide an in-depth evaluation from three perspectives: 1) Commonsense Knowledge, which assesses how well models can understand visual cues and connect to general knowledge; 2) Fine-grained World Knowledge, which tests the model’s skill in reasoning out specific knowledge from images, showcasing their proficiency across various specialized fields; 3) Comprehensive Knowledge with Decision-making Rationales, which examines model’s capability to provide logical explanations for its inference, facilitating a deeper analysis from the interpretability perspective. |
YUNXIN LI et. al. | arxiv-cs.CL | 2023-11-13 |
487 | A Benchmark to Understand The Role of Knowledge Graphs on Large Language Model’s Accuracy for Question Answering on Enterprise SQL Databases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study aims to evaluate the accuracy of LLM-powered question answering systems in the context of enterprise questions and SQL databases, while also exploring the role of knowledge graphs in improving accuracy. To achieve this, we introduce a benchmark comprising an enterprise SQL schema in the insurance domain, a range of enterprise queries encompassing reporting to metrics, and a contextual layer incorporating an ontology and mappings that define a knowledge graph. |
Juan Sequeda; Dean Allemang; Bryon Jacob; | arxiv-cs.AI | 2023-11-13 |
488 | Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present BYOKG, a universal question-answering (QA) system that can operate on any knowledge graph (KG), requires no human-annotated training data, and can be ready to use within a day — attributes that are out-of-scope for current KGQA systems. |
Dhruv Agarwal; Rajarshi Das; Sopan Khosla; Rashmi Gangadharaiah; | arxiv-cs.CL | 2023-11-13 |
489 | CPopQA: Ranking Cultural Concept Popularity By LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: How well an LLM captures the corpus-level statistical trends of concepts for reasoning, especially long-tail ones, is still underexplored. In this study, we introduce a novel few-shot question-answering task (CPopQA) that examines LLMs’ statistical ranking abilities for long-tail cultural concepts (e.g., holidays), with a specific focus on these concepts’ popularity in the United States and the United Kingdom, respectively. |
Ming Jiang; Mansi Joshi; | arxiv-cs.CL | 2023-11-13 |
490 | Hallucination Augmented Recitations for Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Hallucination Augmented Recitations (HAR) for creating counterfactual datasets by utilizing hallucination in LLMs to improve attribution. |
Abdullatif Köksal; Renat Aksitov; Chung-Ching Chang; | arxiv-cs.CL | 2023-11-13 |
491 | A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Challenges arise when these models grapple with understanding multi-hop relations in complex questions or lack the necessary knowledge for a comprehensive response. To address this issue, we introduce the Decompose-and-Query framework (D&Q). |
HEJING CAO et. al. | arxiv-cs.CL | 2023-11-13 |
492 | AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we extend the instruction-tuned Llama-2 model with end-to-end general-purpose speech processing and reasoning abilities while maintaining the wide range of original LLM capabilities, without using any carefully curated paired data. |
YASSIR FATHULLAH et. al. | arxiv-cs.CL | 2023-11-12 |
493 | SELF-EXPLAIN: Teaching Large Language Models to Reason Complex Questions By Themselves Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose SELF-EXPLAIN to generate CoT examples by LLMs inspired by encoding specificity in human memory retrieval. |
Jiachen Zhao; Zonghai Yao; Zhichao Yang; Hong Yu; | arxiv-cs.CL | 2023-11-12 |
494 | On The Robustness of Question Rewriting Systems to Questions of Varying Hardness Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we are interested in the robustness of a QR system to questions varying in rewriting hardness or difficulty. |
Hai Ye; Hwee Tou Ng; Wenjuan Han; | arxiv-cs.CL | 2023-11-12 |
495 | Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Thus, we introduce Knowledgeable Preference AlignmenT (KnowPAT), which constructs two kinds of preference sets to tackle the two issues. |
YICHI ZHANG et. al. | arxiv-cs.CL | 2023-11-11 |
496 | BizBench: A Quantitative Reasoning Benchmark for Business and Finance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce BizBench, a benchmark for evaluating models’ ability to reason about realistic financial problems. |
RIK KONCEL-KEDZIORSKI et. al. | arxiv-cs.CL | 2023-11-11 |
497 | Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Large Multimodal Models (LMMs) have shown promise in vision-language tasks but struggle with high-resolution input and detailed scene understanding. Addressing these challenges, we introduce Monkey to enhance LMM capabilities. |
ZHANG LI et. al. | arxiv-cs.CV | 2023-11-11 |
498 | Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We introduce Lumos, a novel framework for training language agents that employs a unified data format and a modular architecture based on open-source large language models (LLMs). … |
DA YIN et. al. | ArXiv | 2023-11-09 |
499 | SEMQA: Semi-Extractive Multi-Source Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a new QA task for answering multi-answer questions by summarizing multiple diverse sources in a semi-extractive fashion. |
TAL SCHUSTER et. al. | arxiv-cs.CL | 2023-11-08 |
500 | LooGLE: Can Long-Context Language Models Understand Long Contexts? IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Large language models (LLMs), despite their impressive performance in various language tasks, are typically limited to processing texts within context-window size. This limitation … |
Jiaqi Li; Mengmeng Wang; Zilong Zheng; Muhan Zhang; | ArXiv | 2023-11-08 |
501 | NLQxform: A Language Model-based Question to SPARQL Transformer Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: In recent years, scholarly data has grown dramatically in terms of both scale and complexity. It becomes increasingly challenging to retrieve information from scholarly knowledge … |
Ruijie Wang; Zhiruo Zhang; Luca Rossetto; Florian Ruosch; Abraham Bernstein; | ArXiv | 2023-11-08 |
502 | Leveraging Structured Information for Explainable Multi-hop Question Answering and Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we investigate constructing and leveraging extracted semantic structures (graphs) for multi-hop question answering, especially the reasoning process. |
Ruosen Li; Xinya Du; | arxiv-cs.CL | 2023-11-07 |
503 | Adapting Pre-trained Generative Models for Extractive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a novel approach that uses the power of pre-trained generative models to address extractive QA tasks by generating indexes corresponding to context tokens or sentences that form part of the answer. |
Prabir Mallick; Tapas Nayak; Indrajit Bhattacharya; | arxiv-cs.CL | 2023-11-06 |
504 | In-Context Learning for Knowledge Base Question Answering for Unmanned Systems Based on Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we focus on the CCKS2023 Competition of Question Answering with Knowledge Graph Inference for Unmanned Systems. |
Yunlong Chen; Yaming Zhang; Jianfei Yu; Li Yang; Rui Xia; | arxiv-cs.CL | 2023-11-06 |
505 | Divide & Conquer for Entailment-aware Multi-hop Evidence Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we demonstrate that textual entailment relation is another important relevance dimension that should be considered. |
Fan Luo; Mihai Surdeanu; | arxiv-cs.CL | 2023-11-05 |
506 | Causal Question Answering with Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Hence, in this paper, we aim to answer causal questions with a causality graph, a large-scale dataset of causal relations between noun phrases along with the relations’ provenance data. |
Lukas Blübaum; Stefan Heindorf; | arxiv-cs.AI | 2023-11-05 |
507 | AI-TA: Towards An Intelligent Question-Answer Teaching Assistant Using Open-Source LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the challenges of scalable and intelligent question-answering (QA), we introduce an innovative solution that leverages open-source Large Language Models (LLMs) from the LLaMA-2 family to ensure data privacy. |
Yann Hicke; Anmol Agarwal; Qianou Ma; Paul Denny; | arxiv-cs.LG | 2023-11-05 |
508 | Perturbation-based Active Learning for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a perturbation-based active learning acquisition strategy and demonstrate it is more effective than existing commonly used strategies. |
Fan Luo; Mihai Surdeanu; | arxiv-cs.CL | 2023-11-04 |
509 | SAC3: Reliable Hallucination Detection in Black-Box Language Models Via Semantic-aware Cross-check Consistency IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To achieve this goal, we re-examine existing detection approaches based on the self-consistency of LMs and uncover two types of hallucinations resulting from 1) question-level and 2) model-level, which cannot be effectively identified through self-consistency check alone. Building upon this discovery, we propose a novel sampling-based method, i.e., semantic-aware cross-check consistency (SAC3) that expands on the principle of self-consistency checking. |
Jiaxin Zhang; Zhuohang Li; Kamalika Das; Bradley A. Malin; Sricharan Kumar; | arxiv-cs.CL | 2023-11-03 |
510 | Hint-enhanced In-Context Learning Wakes Large Language Models Up for Knowledge-intensive Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, under the standard ICL setting, LLMs may sometimes neglect query-related information in demonstrations, leading to incorrect predictions. To address this limitation, we propose a new paradigm called Hint-enhanced In-Context Learning (HICL) to explore the power of ICL in open-domain question answering, an important form in knowledge-intensive tasks. |
YIFAN WANG et. al. | arxiv-cs.CL | 2023-11-03 |
511 | Long Story Short: A Summarize-then-Search Method for Long Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This capability has been particularly effective in settings such as narrative question answering, where the diversity of tasks is immense, but the available supervision data is small. In this work, we investigate if such language models can extend their zero-shot reasoning abilities to long multimodal narratives in multimedia content such as drama, movies, and animation, where the story plays an essential role. |
Jiwan Chung; Youngjae Yu; | arxiv-cs.CV | 2023-11-02 |
512 | CASE: Commonsense-Augmented Score with An Expanded Answer Space Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose CASE, a Commonsense-Augmented Score with an Expanded Answer Space. |
Wenkai Chen; Sahithya Ravi; Vered Shwartz; | arxiv-cs.CL | 2023-11-02 |
513 | Predicting Question-Answering Performance of Large Language Models Through Semantic Consistency Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We address the task of assessing question-answering (QA) semantic consistency of contemporary large language models (LLMs) by manually creating a benchmark dataset with high-quality paraphrases for factual questions, and release the dataset to the community. |
Ella Rabinovich; Samuel Ackerman; Orna Raz; Eitan Farchi; Ateret Anaby-Tavor; | arxiv-cs.CL | 2023-11-02 |
514 | From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The work aims to navigate both beginners and experts by shedding light on the potential avenues of research and expanding the boundaries of the field. |
Md Farhan Ishmam; Md Sakib Hossain Shovon; M. F. Mridha; Nilanjan Dey; | arxiv-cs.CV | 2023-11-01 |
515 | Chinese Mineral Question and Answering System Based on Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View |
CHENGJIAN LIU et. al. | Expert Syst. Appl. | 2023-11-01 |
516 | VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose VQA-GEN, the first ever multi-modal benchmark dataset for distribution shift generated through a shift induced pipeline. |
Suraj Jyothi Unni; Raha Moraffah; Huan Liu; | arxiv-cs.CV | 2023-11-01 |
517 | DIVKNOWQA: Assessing The Reasoning Ability of LLMs Via Open-Domain Question Answering Over Knowledge Base and Text Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when solely relying on their internal knowledge, especially … |
WENTING ZHAO et. al. | arxiv-cs.CL | 2023-10-31 |
518 | BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We created the BioInstruct, comprising 25,005 instructions to instruction-tune LLMs(LLaMA 1 & 2, 7B & 13B version). |
Hieu Tran; Zhichao Yang; Zonghai Yao; Hong Yu; | arxiv-cs.CL | 2023-10-30 |
519 | Split-NER: Named Entity Recognition Via Two Question-Answering-based Classifications Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we address the NER problem by splitting it into two logical sub-tasks: (1) Span Detection which simply extracts entity mention spans irrespective of entity type; (2) Span Classification which classifies the spans into their entity types. |
Jatin Arora; Youngja Park; | arxiv-cs.CL | 2023-10-30 |
520 | Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a multimodal framework that uses language guidance (LG) in the form of rationales, image captions, scene graphs, etc to answer questions more accurately. |
Deepanway Ghosal; Navonil Majumder; Roy Ka-Wei Lee; Rada Mihalcea; Soujanya Poria; | arxiv-cs.CV | 2023-10-30 |
521 | Multimodal ChatGPT for Medical Applications: An Experimental Study of GPT-4V IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we critically evaluate the capabilities of the state-of-the-art multimodal large language model, i.e., GPT-4 with Vision (GPT-4V), on Visual Question Answering (VQA) task. |
ZHILING YAN et. al. | arxiv-cs.CV | 2023-10-29 |
522 | EHRTutor: Enhancing Patient Understanding of Discharge Instructions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents EHRTutor, an innovative multi-component framework leveraging the Large Language Model (LLM) for patient education through conversational question-answering. |
Zihao Zhang; Zonghai Yao; Huixue Zhou; Feiyun ouyang; Hong Yu; | arxiv-cs.CL | 2023-10-29 |
523 | DCQA: Document-Level Chart Question Answering Towards Complex Reasoning and Common-Sense Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a novel task named document-level chart question answering (DCQA). |
ANRAN WU et. al. | arxiv-cs.AI | 2023-10-29 |
524 | Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal Imagery Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a curriculum learning strategy to enhance the performance of multimodal deep learning models. |
Huseyin Fuat Alsan; Taner Arsan; | arxiv-cs.CV | 2023-10-29 |
525 | Prompt-Engineering and Transformer-based Question Generation and Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this research, we finetuned a pretrained distilBERT model on the SQuAD question answering dataset to generate questions. |
Rubaba Amyeen; | arxiv-cs.CL | 2023-10-28 |
526 | EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce EHRXQA, a novel multi-modal question answering dataset combining structured EHRs and chest X-ray images. |
SEONGSU BAE et. al. | arxiv-cs.CL | 2023-10-28 |
527 | 3D-Aware Visual Question Answering About Parts, Poses and Occlusions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce the task of 3D-aware VQA, which focuses on challenging questions that require a compositional reasoning over the 3D structure of visual scenes. |
Xingrui Wang; Wufei Ma; Zhuowan Li; Adam Kortylewski; Alan Yuille; | arxiv-cs.CV | 2023-10-27 |
528 | ViCLEVR: A Visual Reasoning Dataset and Hybrid Multimodal Fusion Model for Visual Question Answering in Vietnamese Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Neural models for VQA have made remarkable progress on large-scale datasets, with a primary focus on resource-rich languages like English. To address this, we introduce the ViCLEVR dataset, a pioneering collection for evaluating various visual reasoning capabilities in Vietnamese while mitigating biases. |
Khiem Vinh Tran; Hao Phu Phan; Kiet Van Nguyen; Ngan Luu Thuy Nguyen; | arxiv-cs.CL | 2023-10-27 |
529 | Detrimental Contexts in Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we analyze how passages can have a detrimental effect on retrieve-then-read architectures used in question answering. |
Philhoon Oh; James Thorne; | arxiv-cs.CL | 2023-10-27 |
530 | Knowledge Corpus Error in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This study revisits the conventional formulation of QA and introduces the concept of knowledge corpus error. |
Yejoon Lee; Philhoon Oh; James Thorne; | arxiv-cs.CL | 2023-10-27 |
531 | Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We identify and address several reliability challenges in existing QG/A work: (a) QG questions should respect the prompt (avoiding hallucinations, duplications, and omissions) and (b) VQA answers should be consistent (not asserting that there is no motorcycle in an image while also claiming the motorcycle is blue). We address these issues with Davidsonian Scene Graph (DSG), an empirically grounded evaluation framework inspired by formal semantics, which is adaptable to any QG/A frameworks. |
JAEMIN CHO et. al. | arxiv-cs.CV | 2023-10-27 |
532 | Incorporating Probing Signals Into Multimodal Machine Translation Via Visual Question-Answering Pairs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents an in-depth study of multimodal machine translation (MMT), examining the prevailing understanding that MMT systems exhibit decreased sensitivity to visual information when text inputs are complete. |
YUXIN ZUO et. al. | arxiv-cs.CL | 2023-10-26 |
533 | Finetuning Language Models for Multimodal Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: To achieve multi-modal intelligence, AI must be able to process and respond to inputs from multimodal sources. However, many current question answering models are limited to … |
XIN ZHANG et. al. | Proceedings of the 31st ACM International Conference on … | 2023-10-26 |
534 | In-Context Ability Transfer for Question Decomposition in Complex QA Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Answering complex questions is a challenging task that requires question decomposition and multistep reasoning for arriving at the solution. While existing supervised and … |
V. Venktesh; Sourangshu Bhattacharya; Avishek Anand; | ArXiv | 2023-10-26 |
535 | Improving Zero-shot Reader By Reducing Distractions from Irrelevant Documents in Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study aims at the feasibility of a zero-shot reader that addresses the challenges of computational cost and the need for labeled data. |
Sukmin Cho; Jeongyeon Seo; Soyeong Jeong; Jong C. Park; | arxiv-cs.CL | 2023-10-26 |
536 | Binary State Recognition By Robots Using Visual Question Answering of Pre-Trained Vision-Language Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Until now, these states have been recognized by programmatically describing the state of a point cloud or raw image, by annotating and learning images, by using special sensors, etc. In contrast to these methods, we apply Visual Question Answering (VQA) from a Pre-Trained Vision-Language Model (PTVLM) trained on a large-scale dataset, to such binary state recognition. |
Kento Kawaharazuka; Yoshiki Obinata; Naoaki Kanazawa; Kei Okada; Masayuki Inaba; | arxiv-cs.RO | 2023-10-25 |
537 | 1-PAGER: One Pass Answer Generation and Evidence Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present 1-Pager the first system that answers a question and retrieves evidence using a single Transformer-based model and decoding process. |
Palak Jain; Livio Baldini Soares; Tom Kwiatkowski; | arxiv-cs.CL | 2023-10-25 |
538 | Enhancing Document Information Analysis with Multi-Task Pre-training: A Robust Approach for Information Extraction in Visually-Rich Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a deep learning model tailored for document information analysis, emphasizing document classification, entity relation extraction, and document visual question answering. |
Tofik Ali; Partha Pratim Roy; | arxiv-cs.CV | 2023-10-25 |
539 | Quality > Quantity: Synthetic Corpora from Foundation Models for Closed-Domain Extractive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study extractive question answering within closed domains and introduce the concept of targeted pre-training. |
Saptarshi Sengupta; Connor Heaton; Shreya Ghosh; Preslav Nakov; Prasenjit Mitra; | arxiv-cs.CL | 2023-10-25 |
540 | Exploring Question Decomposition for Zero-Shot VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we show that naive application of model-written decompositions can hurt performance. We introduce a model-driven selective decomposition approach for second-guessing predictions and correcting errors, and validate its effectiveness on eight VQA tasks across three domains, showing consistent improvements in accuracy, including improvements of >20% on medical VQA datasets and boosting the zero-shot performance of BLIP-2 above chance on a VQA reformulation of the challenging Winoground task. |
Zaid Khan; Vijay Kumar BG; Samuel Schulter; Manmohan Chandraker; Yun Fu; | arxiv-cs.CV | 2023-10-25 |
541 | AVIS: Autonomous Visual Information Seeking with Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an autonomous information seeking visual question answering framework, AVIS. |
ZINIU HU et. al. | nips | 2023-10-24 |
542 | BeaverTails: A Human-Preference Dataset for LLM Harmlessness Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the BeaverTails dataset, aimed at fostering research on safety alignment in large language models (LLMs). |
JIAMING JI et. al. | nips | 2023-10-24 |
543 | LoRA: A Logical Reasoning Augmented Dataset for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: VQA tasks and large vision-and-language models aim to tackle reasoning problems, but the accuracy, consistency and fabrication of the generated answers is hard to evaluate in the absence of a VQA dataset that can offer formal, comprehensive and systematic complex logical reasoning questions. To address this gap, we present LoRA, a novel Logical Reasoning Augmented VQA dataset that requires formal and complex description logic reasoning based on a food-and-kitchen knowledge base. |
Jingying Gao; Qi Wu; Alan Blair; Maurice Pagnucco; | nips | 2023-10-24 |
544 | Large Language Models Are Temporal and Causal Reasoners for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we develop LLaMA-VQA by applying Flipped-VQA to LLaMA, and it outperforms both LLMs-based and non-LLMs-based models on five challenging VideoQA benchmarks. |
Dohwan Ko; Ji Soo Lee; Wooyoung Kang; Byungseok Roh; Hyunwoo J. Kim; | arxiv-cs.CV | 2023-10-24 |
545 | Evaluating Open-QA Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a new task, QA Evaluation (QA-Eval) and the corresponding dataset EVOUNA, designed to assess the accuracy of AI-generated answers in relation to standard answers within Open-QA. |
CUNXIANG WANG et. al. | nips | 2023-10-24 |
546 | ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This leaves the vast potential of combining electrocardiogram (ECG) data with these systems largely untapped. To address this gap, we present ECG-QA, the first QA dataset specifically designed for ECG analysis. |
Jungwoo Oh; Seongsu Bae; Gyubok Lee; Joon-myoung Kwon; Edward Choi; | nips | 2023-10-24 |
547 | Emergent Communication in Interactive Sketch Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Vision-based emergent communication (EC) aims to learn to communicate through sketches and demystify the evolution of human communication. |
Zixing Lei; Yiming Zhang; Yuxin Xiong; Siheng Chen; | arxiv-cs.AI | 2023-10-24 |
548 | RealTime QA: What’s The Answer Right Now? IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce RealTime QA, a dynamic question answering (QA) platform that announces questions and evaluates systems on a regular basis (weekly in this version). |
JUNGO KASAI et. al. | nips | 2023-10-24 |
549 | 3D-Aware Visual Question Answering About Parts, Poses and Occlusions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce the task of 3D-aware VQA, which focuses on challenging questions that require a compositional reasoning over the 3D structure of visual scenes. |
Xingrui Wang; Zhuowan Li; Wufei Ma; Adam Kortylewski; Alan Yuille; | nips | 2023-10-24 |
550 | Towards Perceiving Small Visual Details in Zero-shot Visual Question Answering with Multimodal LLMs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we investigate whether MLLMs can perceive small details as well as large details in images. |
Jiarui Zhang; Mahyar Khayatkhoei; Prateek Chhikara; Filip Ilievski; | arxiv-cs.CV | 2023-10-24 |
551 | ToolQA: A Dataset for LLM Question Answering with External Tools IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, current evaluation methods do not distinguish between questions that can be answered using LLMs’ internal knowledge and those that require external information through tool use. To address this issue, we introduce a new dataset called ToolQA, which is designed to faithfully evaluate LLMs’ ability to use external tools for question answering. |
Yuchen Zhuang; Yue Yu; Kuan Wang; Haotian Sun; Chao Zhang; | nips | 2023-10-24 |
552 | Exploring Question Decomposition for Zero-Shot VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we show that naive application of model-written decompositions can hurt performance. We introduce a model-driven _selective decomposition_ approach for second-guessing predictions and correcting errors, and validate its effectiveness on eight VQA tasks across three domains, showing consistent improvements in accuracy, including improvements of >20% on medical VQA datasets and boosting the zero-shot performance of BLIP-2 significantly above chance (+18%) on the challenging Winoground task. |
Zaid Khan; Vijay Kumar B G; Samuel Schulter; Manmohan Chandraker; Yun Fu; | nips | 2023-10-24 |
553 | EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce EgoSchema, a very long-form video question-answering dataset, and benchmark to evaluate long video understanding capabilities of modern vision and language systems. |
Karttikeya Mangalam; Raiymbek Akshulakov; Jitendra Malik; | nips | 2023-10-24 |
554 | EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce EHRXQA, a novel multi-modal question answering dataset for structured EHRs and chest X-ray images. |
SEONGSU BAE et. al. | nips | 2023-10-24 |
555 | Benchmarking Large Language Models on CMExam – A Comprehensive Chinese Medical Exam Dataset IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam, sourced from the Chinese National Medical Licensing Examination. |
JUNLING LIU et. al. | nips | 2023-10-24 |
556 | From Parameter-Efficient to Memory-Efficient Fine-Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first investigate what is a key factor for the success of existing PEFT methods, and realize that it’s essential to preserve the PLM’s starting point when initializing a PEFT method. With this finding, we propose memory-efficient fine-tuning (MEFT) that inserts adapters into a PLM, preserving the PLM’s starting point and making it reversible without additional pre-training. |
Baohao Liao; Shaomu Tan; Christof Monz; | nips | 2023-10-24 |
557 | Foundation Model Is Efficient Multimodal Multitask Model Selector Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although recent-advanced approaches employed lightweight metrics to measure models’ transferability, they often depend heavily on the prior knowledge of a single task, making them inapplicable in a multi-modal multi-task scenario. To tackle this issue, we propose an efficient multitask model selector (EMMS), which employs large-scale foundation models to transform diverse label formats such as categories, texts, and bounding boxes of different downstream tasks into a unified noisy label embedding. |
FANQING MENG et. al. | nips | 2023-10-24 |
558 | TableQAKit: A Comprehensive and Practical Toolkit for Table-based Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces TableQAKit, the first comprehensive toolkit designed specifically for TableQA. |
FANGYU LEI et. al. | arxiv-cs.CL | 2023-10-23 |
559 | Strong and Efficient Baselines for Open Domain Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the State-of-the-Art (SotA) Dense Passage Retrieval (DPR) retriever and Fusion-in-Decoder (FiD) reader pipeline, and show that it significantly underperforms when applied to ODConvQA tasks due to various limitations. |
Andrei C. Coman; Gianni Barlacchi; Adrià de Gispert; | arxiv-cs.CL | 2023-10-23 |
560 | Generative Pre-trained Transformer for Vietnamese Community-based COVID-19 Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel approach by conducting a comparative analysis of different Transformers vs SOTA models in the community-based COVID-19 question answering dataset. |
Tam Minh Vo; Khiem Vinh Tran; | arxiv-cs.CL | 2023-10-23 |
561 | DISC-FinLLM: A Chinese Financial Large Language Model Based on Multiple Experts Fine-tuning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose Multiple Experts Fine-tuning Framework to build a financial large language model (LLM), DISC-FinLLM. |
WEI CHEN et. al. | arxiv-cs.CL | 2023-10-23 |
562 | An In-Context Schema Understanding Method for Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recently, Large Language Models (LLMs) have shown strong capabilities in language understanding and can be used to solve this task. In doing so, a major challenge for LLMs is to overcome the immensity and heterogeneity of knowledge base schemas.Existing methods bypass this challenge by initially employing LLMs to generate drafts of logic forms without schema-specific details.Then, an extra module is used to inject schema information to these drafts.In contrast, in this paper, we propose a simple In-Context Schema Understanding (ICSU) method that enables LLMs to directly understand schemas by leveraging in-context learning. |
YANTAO LIU et. al. | arxiv-cs.CL | 2023-10-22 |
563 | Retrieval-Augmented Chain-of-Thought in Semi-structured Domains Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study explores leveraging the semi-structured nature of legal and financial data to efficiently retrieve relevant context, enabling the use of LLMs for domain-specialized QA. |
Vaibhav Mavi; Abulhair Saparov; Chen Zhao; | arxiv-cs.CL | 2023-10-22 |
564 | Text Fact Transfer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Text style transfer is a prominent task that aims to control the style of text without inherently changing its factual content. To cover more text modification applications, such as adapting past news for current events and repurposing educational materials, we propose the task of text fact transfer, which seeks to transfer the factual content of a source text between topics without modifying its style. |
Nishant Balepur; Jie Huang; Kevin Chen-Chuan Chang; | arxiv-cs.CL | 2023-10-22 |
565 | Comparative Analysis of Open Source and Commercial Embedding Models for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this industry track presentation, we will provide a comprehensive tour of the best performing embedding models for question answering, as determined by the Massive Text Embedding Benchmark1. |
Georgios Balikas; | cikm | 2023-10-21 |
566 | Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Large language models (LLMs) have gained widespread adoption in various natural language processing tasks, including question answering and dialogue systems. However, a major … |
YUYAN CHEN et. al. | Proceedings of the 32nd ACM International Conference on … | 2023-10-21 |
567 | CORD: A Three-Stage Coarse-to-Fine Framework for Relation Detection in Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple and efficient three-stage framework to exploit the coarse-to-fine paradigm. |
Yanzeng Li; Sen Hu; Wenjuan Han; Lei Zou; | cikm | 2023-10-21 |
568 | LittleMu: Deploying An Online Virtual Teaching Assistant Via Heterogeneous Sources Integration and Chain of Teach Prompts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a virtual MOOC teaching assistant, LittleMu with minimum labeled training data, to provide question answering and chit-chat services. |
SHANGQING TU et. al. | cikm | 2023-10-21 |
569 | Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Models for conversational question answering (ConvQA) over knowledge graphs (KGs) are usually trained and tested on benchmarks of gold QA pairs. |
Magdalena Kaiser; Rishiraj Saha Roy; Gerhard Weikum; | arxiv-cs.CL | 2023-10-20 |
570 | Self-prompted Chain-of-Thought on Large Language Models for Open-domain Multi-hop Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Self-prompted Chain-of-Thought (SP-CoT), an automated framework to mass-produce high quality CoTs of LLMs, by LLMs and for LLMs. |
Jinyuan Wang; Junlong Li; Hai Zhao; | arxiv-cs.CL | 2023-10-20 |
571 | SALMONN: Towards Generic Hearing Abilities for Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose SALMONN, a speech audio language music open neural network, built by integrating a pre-trained text-based large language model (LLM) with speech and audio encoders into a single multimodal model. |
CHANGLI TANG et. al. | arxiv-cs.SD | 2023-10-20 |
572 | Test-Time Self-Adaptive Small Language Models for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we show and investigate the capabilities of smaller self-adaptive LMs, only with unlabeled test data. |
Soyeong Jeong; Jinheon Baek; Sukmin Cho; Sung Ju Hwang; Jong C. Park; | arxiv-cs.CL | 2023-10-20 |
573 | MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To enable LLMs to tackle the task in a zero-shot manner, we introduce MoqaGPT, a straightforward and flexible framework. |
Le Zhang; Yihong Wu; Fengran Mo; Jian-Yun Nie; Aishwarya Agrawal; | arxiv-cs.CL | 2023-10-20 |
574 | Automatic Hallucination Assessment for Aligned Large Language Models Via Transferable Adversarial Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, this paper presents AutoDebug, an LLM-based framework to use prompting chaining to generate transferable adversarial attacks in the form of question-answering examples. |
Xiaodong Yu; Hao Cheng; Xiaodong Liu; Dan Roth; Jianfeng Gao; | arxiv-cs.CL | 2023-10-19 |
575 | CLIFT: Analysing Natural Distribution Shift on Question Answering Models in Clinical Domain Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces a new testbed CLIFT (Clinical Shift) for the clinical domain Question-answering task. |
Ankit Pal; | arxiv-cs.CL | 2023-10-19 |
576 | PSYCHIC: A Neuro-Symbolic Framework for Knowledge Graph Question-Answering Grounding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We answer the KGQA over DBLP (DBLP-QUAD) task by proposing a neuro-symbolic (NS) framework based on PSYCHIC, an extractive QA model capable of identifying the query and entities related to a KG question. |
Hanna Abi Akl; | arxiv-cs.AI | 2023-10-19 |
577 | RSAdapter: Adapting Multimodal Models for Remote Sensing Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: These approaches demand significant computational resources and time, and a considerable number of trainable parameters are introduced. To address these challenges, we introduce a novel method known as RSAdapter, which prioritizes runtime and parameter efficiency. |
Yuduo Wang; Pedram Ghamisi; | arxiv-cs.CV | 2023-10-19 |
578 | Time-Aware Representation Learning for Time-Sensitive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, language models have difficulty understanding the relationships between time specifiers, such as ‘after’ and ‘before’, and numbers, since existing QA datasets do not include sufficient time expressions. To address this issue, we propose a Time-Context aware Question Answering (TCQA) framework. |
Jungbin Son; Alice Oh; | arxiv-cs.CL | 2023-10-19 |
579 | Understanding Retrieval Augmentation for Long-Form Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a study of retrieval-augmented language models (LMs) on long-form question answering. |
Hung-Ting Chen; Fangyuan Xu; Shane A. Arora; Eunsol Choi; | arxiv-cs.CL | 2023-10-18 |
580 | Open-ended Commonsense Reasoning with Unrestricted Answer Scope Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base, which does not require task-specific supervision. |
CHEN LING et. al. | arxiv-cs.CL | 2023-10-17 |
581 | Systematic Assessment of Factual Knowledge in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a framework to systematically assess the factual knowledge of LLMs by leveraging knowledge graphs (KGs). |
Linhao Luo; Thuy-Trang Vu; Dinh Phung; Gholamreza Haffari; | arxiv-cs.CL | 2023-10-17 |
582 | Open Information Extraction: A Review of Baseline Techniques, Approaches, and Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It briefly discusses the main approaches and the pros and cons of each method. |
Serafina Kamp; Morteza Fayazi; Zineb Benameur-El; Shuyan Yu; Ronald Dreslinski; | arxiv-cs.IR | 2023-10-17 |
583 | QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, current QA synthesis protocols may introduce noise from the CSKBs and generate ungrammatical questions and false negative options, which impede the model’s ability to generalize. To address these issues, we propose QADYNAMICS, a training dynamics-driven framework for QA diagnostics and refinement. |
HAOCHEN SHI et. al. | arxiv-cs.CL | 2023-10-17 |
584 | Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel framework for adaptation with self-evaluation to improve the selective prediction performance of LLMs. |
JIEFENG CHEN et. al. | arxiv-cs.CL | 2023-10-17 |
585 | Pragmatic Evaluation of Clarifying Questions with Fact-Level Masking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here we present a definition and framework for natural language pragmatic asking of clarifying questions (PACQ), the problem of generating questions that result in answers useful for a reasoning task. |
Matthew Toles; Yukun Huang; Zhou Yu; Luis Gravano; | arxiv-cs.CL | 2023-10-17 |
586 | Will The Prince Get True Love’s Kiss? On The Model Sensitivity to Gender Perturbation Over Fairytale Texts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent studies show that traditional fairytales are rife with harmful gender biases. To help mitigate these gender biases in fairytales, this work aims to assess learned biases of language models by evaluating their robustness against gender perturbations. |
Christina Chance; Da Yin; Dakuo Wang; Kai-Wei Chang; | arxiv-cs.CL | 2023-10-16 |
587 | UNK-VQA: A Dataset and A Probe Into The Abstention Ability of Multi-modal Large Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper aims to bridge the research gap by contributing a comprehensive dataset, called UNK-VQA. |
Yangyang Guo; Fangkai Jiao; Zhiqi Shen; Liqiang Nie; Mohan Kankanhalli; | arxiv-cs.CV | 2023-10-16 |
588 | Intelligent Software Tooling for Improving Software Development Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: One of the main reasons behind this success is the availability of large datasets such as open-source code available through GitHub or image datasets of mobile Graphical User Interfaces (GUIs) with RICO and ReDRAW to be trained on. Therefore, the central research question my dissertation explores is: In what ways can the software development process be improved through leveraging DL techniques on the vast amounts of unstructured software engineering artifacts? |
Nathan Cooper; | arxiv-cs.SE | 2023-10-16 |
589 | Emerging Challenges in Personalized Medicine: Assessing Demographic Effects on Biomedical Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We find that irrelevant demographic information change up to 15% of the answers of a KG-grounded system and up to 23% of the answers of a text-based system, including changes that affect accuracy. |
Sagi Shaier; Kevin Bennett; Lawrence Hunter; Katharina von der Wense; | arxiv-cs.CL | 2023-10-16 |
590 | A Search for Prompts: Generating Structured Answers from Contracts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In many legal processes being able to action on the concrete implication of a legal question can be valuable to automating human review or signalling certain conditions (e.g., alerts around automatic renewal). To support such tasks, we present a form of legal question answering that seeks to return one (or more) fixed answers for a question about a contract clause. |
ADAM ROEGIEST et. al. | arxiv-cs.CV | 2023-10-16 |
591 | Unifying Image Processing As Visual Prompting Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these advances have predominantly concentrated on high-level vision tasks, with less attention paid to low-level vision tasks. To address this issue, we propose a universal model for general image processing that covers image restoration, image enhancement, image feature extraction tasks, etc. |
YIHAO LIU et. al. | arxiv-cs.CV | 2023-10-16 |
592 | Progressive Evidence Refinement for Open-domain Multimodal Retrieval Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Secondly, a gap exists between the feature extraction of evidence and the question, which hinders the model from effectively extracting critical features from the evidence based on the given question. We propose a two-stage framework for evidence retrieval and question-answering to alleviate these issues. |
SHUWEN YANG et. al. | arxiv-cs.AI | 2023-10-14 |
593 | CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose CarExpert, an in-car retrieval-augmented conversational question-answering system leveraging LLMs for different tasks. |
MD RASHAD AL HASAN RONY et. al. | arxiv-cs.CL | 2023-10-14 |
594 | MiniGPT-v2: Large Language Model As A Unified Interface for Vision-language Multi-task Learning IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Towards this objective, we introduce MiniGPT-v2, a model that can be treated as a unified interface for better handling various vision-language tasks. |
JUN CHEN et. al. | arxiv-cs.CV | 2023-10-13 |
595 | ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In the era of large language models (LLMs), we introduce ChatKBQA, a novel generate-then-retrieve KBQA framework built on fine-tuning open-source LLMs such as Llama-2, ChatGLM2 and Baichuan2. |
HAORAN LUO et. al. | arxiv-cs.CL | 2023-10-13 |
596 | Enhancing BERT-Based Visual Question Answering Through Keyword-Driven Sentence Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The goal is to identify the document elements that answer a specific question posed in natural language. This paper describes the PoliTo’s approach to addressing this task, in particular, our best solution explores a text-only approach, leveraging an ad hoc sampling strategy. |
Davide Napolitano; Lorenzo Vaiani; Luca Cagliero; | arxiv-cs.CL | 2023-10-13 |
597 | Open-Set Knowledge-Based Visual Question Answering with Inference Paths Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we confront the challenge of \emph{explainable open-set} KB-VQA, where the system is required to answer questions with entities at wild and retain an explainable reasoning path. |
Jingru Gan; Xinzhe Han; Shuhui Wang; Qingming Huang; | arxiv-cs.LG | 2023-10-12 |
598 | Training Generative Question-Answering on Synthetic Data Obtained from An Instruct-tuned Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a simple and cost-effective method for synthesizing data to train question-answering systems. |
Kosuke Takahashi; Takahiro Omi; Kosuke Arima; Tatsuya Ishigaki; | arxiv-cs.CL | 2023-10-12 |
599 | Low-Resource Clickbait Spoiling for Indonesian Via Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our contributions include the construction of manually labeled clickbait spoiling corpus in Indonesian and an evaluation on using cross-lingual zero-shot question answering-based models to tackle clikcbait spoiling for low-resource language like Indonesian. |
Ni Putu Intan Maharani; Ayu Purwarianti; Alham Fikri Aji; | arxiv-cs.CL | 2023-10-12 |
600 | Question Answering for Electronic Health Records: A Scoping Review of Datasets and Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We searched for articles from January 1st, 2005 to September 30th, 2023 in four digital sources including Google Scholar, ACL Anthology, ACM Digital Library, and PubMed to collect relevant publications on EHR QA. |
Jayetri Bardhan; Kirk Roberts; Daisy Zhe Wang; | arxiv-cs.LG | 2023-10-12 |
601 | Understanding How to Inform Blind and Low-Vision Users About Data Privacy Through Privacy Question Answering Assistants Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We conducted an in-depth qualitative study with 21 US BLV participants to understand their data privacy risk perception and mitigation, as well as their information behaviors related to data privacy. |
YUANYUAN FENG et. al. | arxiv-cs.HC | 2023-10-12 |
602 | From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop a domain KG to leverage cancer-specific biomarker discovery and interactive QA. |
MD. REZAUL KARIM et. al. | arxiv-cs.CL | 2023-10-12 |
603 | QASiNa: Religious Domain Question Answering Using Sirah Nabawiyah Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose the Question Answering Sirah Nabawiyah (QASiNa) dataset, a novel dataset compiled from Sirah Nabawiyah literatures in Indonesian language. |
Muhammad Razif Rizqullah; Ayu Purwarianti; Alham Fikri Aji; | arxiv-cs.CL | 2023-10-12 |
604 | Mitigating Bias for Question Answering Models By Tracking Bias Influence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose BMBI, an approach to mitigate the bias of multiple-choice QA models. |
MINGYU DEREK MA et. al. | arxiv-cs.CL | 2023-10-12 |
605 | Mini-DALLE3: Interactive Text to Image By Prompting Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In addressing the iT2I problem, we present a simple approach that augments LLMs for iT2I with prompting techniques and off-the-shelf T2I models. |
Lai Zeqiang; Zhu Xizhou; Dai Jifeng; Qiao Yu; Wang Wenhai; | arxiv-cs.AI | 2023-10-11 |
606 | OpsEval: A Comprehensive IT Operations Benchmark Suite for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present OpsEval, a comprehensive task-oriented Ops benchmark designed for LLMs. |
YUHE LIU et. al. | arxiv-cs.AI | 2023-10-11 |
607 | Exploring The Landscape of Large Language Models in Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With the rapid development of new large language models (LLMs), each claiming to surpass previous models, an overall picture of medical LLM research can be elusive. To address this challenge, we benchmark a range of top LLMs and identify consistent patterns which appear across models. |
Andrew M. Bean; Karolina Korgul; Felix Krones; Robert McCraith; Adam Mahdi; | arxiv-cs.CL | 2023-10-11 |
608 | Framework for Question-Answering in Sanskrit Through Automated Construction of Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we target the problem of building knowledge graphs for particular types of relationships from sa\d{m}sk\d{r}ta texts. |
Hrishikesh Terdalkar; Arnab Bhattacharya; | arxiv-cs.CL | 2023-10-11 |
609 | QACHECK: A Demonstration System for Question-Guided Multi-Hop Fact-Checking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing fact-checking systems often lack transparency in their decision-making, making it challenging for users to comprehend their reasoning process. To address this, we propose the Question-guided Multi-hop Fact-Checking (QACHECK) system, which guides the model’s reasoning process by asking a series of questions critical for verifying a claim. |
Liangming Pan; Xinyuan Lu; Min-Yen Kan; Preslav Nakov; | arxiv-cs.CL | 2023-10-11 |
610 | Answer Candidate Type Selection: Text-to-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the capacity of the models is limited and the quality decreases for questions with less popular entities. In this paper, we present a novel approach which works on top of the pre-trained Text-to-Text QA system to address this issue. |
MIKHAIL SALNIKOV et. al. | arxiv-cs.CL | 2023-10-10 |
611 | Jaeger: A Concatenation-Based Multi-Transformer VQA Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although there has been encouraging progress in document-based question answering due to the utilization of large language and open-world prior models\cite{1}, several challenges persist, including prolonged response times, extended inference durations, and imprecision in matching. In order to overcome these challenges, we propose Jaegar, a concatenation-based multi-transformer VQA model. |
Jieting Long; Zewei Shi; Penghao Jiang; Yidong Gan; | arxiv-cs.CL | 2023-10-10 |
612 | Solution for SMART-101 Challenge of ICCV Multi-modal Algorithmic Reasoning Task 2023 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present our solution to a Multi-modal Algorithmic Reasoning Task: SMART-101 Challenge. |
XIANGYU WU et. al. | arxiv-cs.CV | 2023-10-10 |
613 | MemSum-DQA: Adapting An Efficient Long Document Extractive Summarizer for Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce MemSum-DQA, an efficient system for document question answering (DQA) that leverages MemSum, a long document extractive summarizer. |
Nianlong Gu; Yingqiang Gao; Richard H. R. Hahnloser; | arxiv-cs.CL | 2023-10-10 |
614 | Towards Mitigating Hallucination in Large Language Models Via Self-Reflection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our investigation centers on the identification and comprehension of common problematic answers, with a specific emphasis on hallucination. To tackle this challenge, we present an interactive self-reflection methodology that incorporates knowledge acquisition and answer generation. |
ZIWEI JI et. al. | arxiv-cs.CL | 2023-10-09 |
615 | Causal Reasoning Through Two Layers of Cognition for Improving Generalization in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, diverse interpretations of the input lead to various modes of answer generation, highlighting the role of causal reasoning between interpreting and answering steps in VQA. Through this lens, we propose Cognitive pathways VQA (CopVQA) improving the multimodal predictions by emphasizing causal reasoning factors. |
Trang Nguyen; Naoaki Okazaki; | arxiv-cs.AI | 2023-10-09 |
616 | FireAct: Toward Language Agent Fine-tuning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate and argue for the overlooked direction of fine-tuning LMs to obtain language agents. |
BAIAN CHEN et. al. | arxiv-cs.CL | 2023-10-09 |
617 | Retrieval-Generation Synergy Augmented Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: One is to retrieve from an external knowledge base, and the other is to utilize large language models to generate documents. |
Zhangyin Feng; Xiaocheng Feng; Dezhi Zhao; Maojin Yang; Bing Qin; | arxiv-cs.CL | 2023-10-08 |
618 | Revisiting Large Language Models As Zero-shot Relation Extractors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the summarize-and-ask (\textsc{SumAsk}) prompting, a simple prompt recursively using LLMs to transform RE inputs to the effective question answering (QA) format. |
Guozheng Li; Peng Wang; Wenjun Ke; | arxiv-cs.AI | 2023-10-08 |
619 | MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to select the most informative data for fine-tuning, thereby improving the efficiency of the fine-tuning process with comparative or even better accuracy on the open-domain QA task. |
XIUSI CHEN et. al. | arxiv-cs.CL | 2023-10-08 |
620 | Analyzing Zero-Shot Abilities of Vision-Language Models on Video Understanding Tasks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Therefore, the pertinent question to ask is: Can image-text models be adapted to video tasks and is there any benefit to using these models over pretraining directly on videos? In this work, we focus on this question by proposing a detailed study on the generalization abilities of image-text models when evaluated on video understanding tasks in a zero-shot setting. |
Avinash Madasu; Anahita Bhiwandiwalla; Vasudev Lal; | arxiv-cs.CV | 2023-10-07 |
621 | Analysis of The Reasoning with Redundant Information Provided Ability of Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The study designed a modified version of the grade school math 8K (GSM-8K) dataset which has several variants focusing on different attributes of redundant information. |
Wenbei Xie; | arxiv-cs.CL | 2023-10-06 |
622 | Policy-Gradient Training of Language Models for Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This reliance on heuristics stems from the fact that the contrastive loss itself is heuristic and does not directly optimize the downstream metrics of decision quality at the end of the processing pipeline. To address this issue, we introduce Neural PG-RANK, a novel training algorithm that learns to rank by instantiating a LLM as a Plackett-Luce ranking policy. |
Ge Gao; Jonathan D. Chang; Claire Cardie; Kianté Brantley; Thorsten Joachim; | arxiv-cs.CL | 2023-10-06 |
623 | LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we introduce a new LLM-Coordination Benchmark aimed at a detailed analysis of LLMs within the context of Pure Coordination Games, where participating agents need to cooperate for the most gain. |
Saaket Agashe; Yue Fan; Anthony Reyna; Xin Eric Wang; | arxiv-cs.CL | 2023-10-05 |
624 | Integrating UMLS Knowledge Into Large Language Models for Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our research, we develop an augmented LLM framework based on the Unified Medical Language System (UMLS), aiming to better serve the healthcare community. |
RUI YANG et. al. | arxiv-cs.CL | 2023-10-04 |
625 | Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we designed prompts that retrieve and use content from a high-quality open-source math textbook to generate responses to real student questions. |
ZACHARY LEVONIAN et. al. | arxiv-cs.CL | 2023-10-04 |
626 | Multimodal Question Answering for Unified Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Due to the diversity of tasks and settings, most current MIE models are task-specific and data-intensive, which limits their generalization to real-world scenarios with diverse task requirements and limited labeled data. To address these issues, we propose a novel multimodal question answering (MQA) framework to unify three MIE tasks by reformulating them into a unified span extraction and multi-choice QA pipeline. |
Yuxuan Sun; Kai Zhang; Yu Su; | arxiv-cs.CL | 2023-10-04 |
627 | Language Models As Knowledge Bases for Visual Word Sense Disambiguation Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Visual Word Sense Disambiguation (VWSD) is a novel challenging task that lies between linguistic sense disambiguation and fine-grained multimodal retrieval. The recent … |
Anastasia Kritharoula; Maria Lymperaiou; Giorgos Stamou; | arxiv-cs.CL | 2023-10-03 |
628 | Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a unique object-level multimodal LLM architecture that merges vectorized numeric modalities with a pre-trained LLM to improve context understanding in driving situations. |
LONG CHEN et. al. | arxiv-cs.RO | 2023-10-03 |
629 | On The Cognition of Visual Question Answering Models and Human Intelligence: A Comparative Study Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To inspect the association of VQA models to human cognition, we designed a survey to record human thinking process and analyzed VQA models by comparing the outputs and attention maps with those of humans. |
Liben Chen; Long Chen; Tian Ellison-Chen; Zhuoyuan Xu; | arxiv-cs.CV | 2023-10-03 |
630 | Think Before You Speak: Training Language Models With Pause Tokens IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We empirically evaluate $\textit{pause-training}$ on decoder-only models of 1B and 130M parameters with causal pretraining on C4, and on downstream tasks covering reasoning, question-answering, general understanding and fact recall. |
SACHIN GOYAL et. al. | arxiv-cs.CL | 2023-10-03 |
631 | An Empirical Study of ChatGPT-3.5 on Question Answering and Code Maintenance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Ever since the launch of ChatGPT in 2022, a rising concern is whether ChatGPT will replace programmers and kill jobs. Motivated by this widespread concern, we conducted an empirical study to systematically compare ChatGPT against programmers in question-answering and software-maintaining. |
MD MAHIR ASEF KABIR et. al. | arxiv-cs.SE | 2023-10-03 |
632 | SelfGraphVQA: A Self-Supervised Graph Neural Network for Scene-based Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we demonstrate that despite the effectiveness of scene graphs in VQA tasks, current methods that utilize idealized annotated scene graphs struggle to generalize when using predicted scene graphs extracted from images. To address this issue, we introduce the SelfGraphVQA framework. |
Bruno Souza; Marius Aasan; Helio Pedrini; Adín Ramírez Rivera; | arxiv-cs.CV | 2023-10-03 |
633 | External Commonsense Knowledge As A Modality for Social Intelligence Question-Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Artificial Social Intelligence (ASI) refers to the perception and understanding of social interactions. It involves the usage of contextual information about social cues to … |
Sanika Natu; Shounak Sural; Sulagna Sarkar; | 2023 IEEE/CVF International Conference on Computer Vision … | 2023-10-02 |
634 | Multi-Modal Correlated Network with Emotional Reasoning Knowledge for Social Intelligence Question-Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The capacity for social reasoning is essential to the development of social intelligence in humans, which we easily acquire through study and experience. The acquisition of such … |
Baijun Xie; Chung Hyuk Park; | 2023 IEEE/CVF International Conference on Computer Vision … | 2023-10-02 |
635 | Generating Explanations in Medical Question-Answering By Expectation Maximization Inference Over Evidence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To do so, we propose a novel approach for generating natural language explanations for answers predicted by medical QA systems. |
Wei Sun; Mingxiao Li; Damien Sileo; Jesse Davis; Marie-Francine Moens; | arxiv-cs.CL | 2023-10-02 |
636 | Human Mobility Question Answering (Vision Paper) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Mining human mobility data is crucial for various applications such as smart city planning, pandemic management, and personalised recommendation system. In this paper, we aim to tackle this gap and introduce a novel task, that is, human mobility question answering (MobQA). |
Hao Xue; Flora D. Salim; | arxiv-cs.CL | 2023-10-02 |
637 | ReAcTable: Enhancing ReAct for Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Nonetheless, a conspicuous gap exists in the research landscape, where there is limited exploration of how innovative foundational research, which integrates incremental reasoning with external tools in the context of LLMs, as exemplified by the ReAct paradigm, could potentially bring advantages to the TQA task. In this paper, we aim to fill this gap, by introducing ReAcTable (ReAct for Table Question Answering tasks), a framework inspired by the ReAct paradigm that is carefully enhanced to address the challenges uniquely appearing in TQA tasks such as interpreting complex data semantics, dealing with errors generated by inconsistent data and generating intricate data transformations. |
YUNJIA ZHANG et. al. | arxiv-cs.DB | 2023-10-01 |
638 | Question Answering Models for Human-machine Interaction in The Manufacturing Industry Related Papers Related Patents Related Grants Related Venues Related Experts View |
Eneko Ruiz; M. Inés Torres; A. del Pozo; | Comput. Ind. | 2023-10-01 |
639 | Understanding AI Cognition: A Neural Module for Inference Inspired By Human Memory Mechanisms Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: How humans and machines make sense of current inputs for relation reasoning and question-answering while putting the perceived information into context of our past memories, has … |
Xiangyu Zeng; Jie Lin; Piao Hu; Ruizheng Huang; Zhicheng Zhang; | ArXiv | 2023-10-01 |
640 | Event-Oriented Visual Question Answering: The E-VQA Dataset and Benchmark Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Visual question answering (VQA) is a challenging task that reasons over questions on images with knowledge. A prerequisite for VQA is the availability of annotated datasets, while … |
Zhenguo Yang; Jiale Xiang; Jiuxiang You; Qing Li; Wenyin Liu; | IEEE Transactions on Knowledge and Data Engineering | 2023-10-01 |
641 | Robust Visual Question Answering Via Semantic Cross Modal Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View |
Akib Mashrur; Wei Luo; Nayyar A. Zaidi; Antonio Robles-Kelly; | Comput. Vis. Image Underst. | 2023-10-01 |
642 | Testing The Limits of Unified Sequence to Sequence LLM Pretraining on Diverse Table Data Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To that end, we attempt at creating a shared modeling approach in the pretraining stage with encoder-decoder style LLMs that can cater to diverse tasks. We evaluate our approach that continually pretrains and finetunes different model families of T5 with data from tables and surrounding context, on these downstream tasks at different model scales. |
Soumajyoti Sarkar; Leonard Lausen; | arxiv-cs.CL | 2023-10-01 |
643 | Question-Answering Model for Schizophrenia Symptoms and Their Impact on Daily Life Using Mental Health Forums Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The purpose of this paper is to present a new methodology for building a medical dataset and obtain a QA model for analysis of symptoms and impact on daily life for a specific disease domain. |
Christian Internò; Eloisa Ambrosini; | arxiv-cs.LG | 2023-09-30 |
644 | Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes Fine-grained Late-interaction Multi-modal Retrieval (FLMR) which significantly improves knowledge retrieval in RA-VQA. |
Weizhe Lin; Jinghong Chen; Jingbiao Mei; Alexandru Coca; Bill Byrne; | arxiv-cs.CL | 2023-09-29 |
645 | Promoting Generalized Cross-lingual Question Answering in Few-resource Scenarios Via Self-knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Beyond performance improvements, we offer valuable insights through comprehensive analyses and an ablation study, further substantiating the benefits and constraints of our approach. |
Casimiro Pio Carrino; Carlos Escolano; José A. R. Fonollosa; | arxiv-cs.CL | 2023-09-29 |
646 | Question Answering Over Knowledge Graphs Using BERT Based Relation Mapping Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: A knowledge graph (KG) is a structured form of knowledge describing real‐world entities, properties and relationships as a graph. Question answering over knowledge graphs (KGQA) … |
S. C. M.; JayaramanPrem Prakash; Pramod Kumar Singh; | Expert Systems | 2023-09-29 |
647 | Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose an end-to-end methodology designed to generate long-form answers to any statutory law questions, utilizing a retrieve-then-read pipeline. |
Antoine Louis; Gijs van Dijck; Gerasimos Spanakis; | arxiv-cs.CL | 2023-09-29 |
648 | Using Weak Supervision and Data Augmentation in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore the roles weak supervision and data augmentation play in training deep neural network QA models. |
Chumki Basu; Himanshu Garg; Allen McIntosh; Sezai Sablak; John R. Wullert II; | arxiv-cs.CL | 2023-09-28 |
649 | Toloka Visual Question Answering Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present Toloka Visual Question Answering, a new crowdsourced dataset allowing comparing performance of machine learning systems against human level of expertise in the grounding visual question answering task. |
Dmitry Ustalov; Nikita Pavlichenko; Sergey Koshelev; Daniil Likhobaba; Alisa Smirnova; | arxiv-cs.CV | 2023-09-28 |
650 | Spider4SPARQL: A Complex Benchmark for Evaluating Knowledge Graph Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce Spider4SPARQL – a new SPARQL benchmark dataset featuring 9,693 previously existing manually generated NL questions and 4,721 unique, novel, and complex SPARQL queries of varying complexity. |
Catherine Kosten; Philippe Cudré-Mauroux; Kurt Stockinger; | arxiv-cs.CL | 2023-09-28 |
651 | VDC: Versatile Data Cleanser Based on Visual-Linguistic Inconsistency By Multimodal Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing detectors only focus on detecting poisoned samples or noisy labels, that are often prone to weak generalization when dealing with dirty samples from other domains.In this paper, we find a commonality of various dirty samples is visual-linguistic inconsistency between images and associated labels. To capture the semantic inconsistency between modalities, we propose versatile data cleanser (VDC) leveraging the surpassing capabilities of multimodal large language models (MLLM) in cross-modal alignment and reasoning.It consists of three consecutive modules: the visual question generation module to generate insightful questions about the image; the visual question answering module to acquire the semantics of the visual content by answering the questions with MLLM; followed by the visual answer evaluation module to evaluate the inconsistency.Extensive experiments demonstrate its superior performance and generalization to various categories and types of dirty samples. |
Zihao Zhu; Mingda Zhang; Shaokui Wei; Bingzhe Wu; Baoyuan Wu; | arxiv-cs.CV | 2023-09-28 |
652 | Open-vocabulary Video Question Answering: A New Benchmark for Evaluating The Generalizability of Video Question Answering Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We hence propose a new benchmark, Open-vocabulary Video Question Answering (OVQA), to measure the generalizability of VideoQA models by considering rare and unseen answers. |
DOHWAN KO et. al. | iccv | 2023-09-27 |
653 | PromptCap: Prompt-Guided Image Captioning for VQA with GPT-3 IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Generic image captions often miss visual details essential for the LM to answer visual questions correctly. To address this challenge, we propose PromptCap (Prompt-guided image Captioning), a captioning model designed to serve as a better connector between images and black-box LMs. |
YUSHI HU et. al. | iccv | 2023-09-27 |
654 | VQA-GNN: Reasoning with Multimodal Knowledge Via Graph Neural Networks for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To perform more expressive reasoning, we propose VQA-GNN, a new VQA model that performs bidirectional fusion between unstructured and structured multimodal knowledge to obtain unified knowledge representations. |
Yanan Wang; Michihiro Yasunaga; Hongyu Ren; Shinya Wada; Jure Leskovec; | iccv | 2023-09-27 |
655 | Variational Causal Inference Network for Explanatory Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, they neglect the complex relationships among question words, visual regions, and explanation tokens. To address these issues, we propose a Variational Causal Inference Network (VCIN) that establishes the causal correlation between predicted answers and explanations, and captures cross-modal relationships to generate rational explanations. |
Dizhan Xue; Shengsheng Qian; Changsheng Xu; | iccv | 2023-09-27 |
656 | Knowledge Proxy Intervention for Deconfounded Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle the challenge that the confounder in VideoQA is unobserved and non-enumerable in general, we propose a model-agnostic framework called Knowledge Proxy Intervention (KPI), which introduces an extra knowledge proxy variable in the causal graph to cut the backdoor path and remove the confounder. |
Jiangtong Li; Li Niu; Liqing Zhang; | iccv | 2023-09-27 |
657 | Question Answering Using Deep Learning in Low Resource Indian Language Marathi Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we investigate different transformer models for creating a reading comprehension-based Marathi question answering system. |
Dhiraj Amin; Sharvari Govilkar; Sagar Kulkarni; | arxiv-cs.CL | 2023-09-27 |
658 | Zero-Shot and Few-Shot Video Question Answering with Multi-Modal Prompts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, adapting pretrained models on limited data presents challenges such as overfitting, catastrophic forgetting, and the cross-modal gap between vision and language. We introduce a parameter-efficient method to address these challenges, combining multimodal prompt learning and a transformer-based mapping network, while keeping the pretrained models frozen. |
Deniz Engin; Yannis Avrithis; | arxiv-cs.CV | 2023-09-27 |
659 | Decouple Before Interact: Multi-Modal Prompt Learning for Continual Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, neglecting the interactions between modalities will lead to poor performance. To tackle these challenging issues, we propose a comprehensive formulation for CL-VQA from the perspective of multi-modal vision-language fusion. |
ZI QIAN et. al. | iccv | 2023-09-27 |
660 | Simple Baselines for Interactive Video Retrieval with Questions and Answers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recently, there has been renewed interest in interactive systems to enhance retrieval, but existing approaches are complex and deliver limited gains in performance. In this work, we revisit this topic and propose several simple yet effective baselines for interactive video retrieval via question-answering. |
Kaiqu Liang; Samuel Albanie; | iccv | 2023-09-27 |
661 | VQA Therapy: Exploring Answer Differences By Visually Grounding Answers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Given that different people can provide different answers to a visual question, we aim to better understand why with answer groundings. |
Chongyan Chen; Samreen Anjum; Danna Gurari; | iccv | 2023-09-27 |
662 | Encyclopedic VQA: Visual Questions About Detailed Properties of Fine-Grained Categories Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose Encyclopedic-VQA, a large scale visual question answering (VQA) dataset featuring visual questions about detailed properties of fine-grained categories and instances. |
THOMAS MENSINK et. al. | iccv | 2023-09-27 |
663 | TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on this approach, we introduce TIFA v1.0, a benchmark consisting of 4K diverse text inputs and 25K questions across 12 categories (object, counting, etc.). |
YUSHI HU et. al. | iccv | 2023-09-27 |
664 | Toward Multi-Granularity Decision-Making: Explicit Visual Reasoning with Hierarchical Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To fill the gap, this paper makes progresses from two distinct perspectives: (1) It presents a Hierarchical Concept Graph (HCG) that discriminates and associates multi-granularity concepts with a multi-layered hierarchical structure, aligning visual observations with knowledge across different levels to alleviate data biases. |
Yifeng Zhang; Shi Chen; Qi Zhao; | iccv | 2023-09-27 |
665 | Discovering Spatio-Temporal Rationales for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To tackle the challenge, we highlight the importance of identifying question-critical temporal moments and spatial objects from the vast amount of video content. Towards this, we propose a Spatio-Temporal Rationalizer (STR), a differentiable selection module that adaptively collects question-critical moments and objects using cross-modal interaction. |
Yicong Li; Junbin Xiao; Chun Feng; Xiang Wang; Tat-Seng Chua; | iccv | 2023-09-27 |
666 | MedEdit: Model Editing for Medical Question Answering with External Knowledge Bases IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Large Language Models (LLMs), although powerful in general domains, often perform poorly on domain-specific tasks like medical question answering (QA). |
YUCHENG SHI et. al. | arxiv-cs.CL | 2023-09-27 |
667 | Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we demonstrate that factual knowledge is imperative for the performance of ICL in three core facets: the inherent knowledge learned in LLMs, the factual knowledge derived from the selected in-context examples, and the knowledge biases in LLMs for output generation. |
Jianing Wang; Chengyu Wang; Chuanqi Tan; Jun Huang; Ming Gao; | arxiv-cs.CL | 2023-09-26 |
668 | Legal Question-Answering in The Indian Context: Efficacy, Challenges, and Potential of Modern AI Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Legal QA platforms bear the promise to metamorphose the manner in which legal experts engage with jurisprudential documents. In this exposition, we embark on a comparative exploration of contemporary AI frameworks, gauging their adeptness in catering to the unique demands of the Indian legal milieu, with a keen emphasis on Indian Legal Question Answering (AILQA). |
Shubham Kumar Nigam; Shubham Kumar Mishra; Ayush Kumar Mishra; Noel Shallum; Arnab Bhattacharya; | arxiv-cs.CL | 2023-09-26 |
669 | Question-Answering Approach to Evaluating Legal Summaries Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel legal summarization evaluation framework that utilizes GPT-4 to generate a set of question-answer pairs that cover main points and information in the reference summary. |
Huihui Xu; Kevin Ashley; | arxiv-cs.CL | 2023-09-26 |
670 | Fine-tuning and Aligning Question Answering Models for Complex Information Extraction Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we propose an approach that uses and integrates extractive QA models for improved feature extraction of German business documents such as insurance reports or medical leaflets into a document analysis solution. |
Matthias Engelbach; Dennis Klau; Felix Scheerer; Jens Drawehn; Maximilien Kintz; | arxiv-cs.CL | 2023-09-26 |
671 | Does The most Sinfully Decadent Cake Ever Taste Good? Answering Yes/No Questions from Figurative Contexts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate the robustness of Question Answering (QA) models on figurative text. |
Geetanjali Rakshit; Jeffrey Flanigan; | arxiv-cs.CL | 2023-09-24 |
672 | Diversifying Question Generation Over Knowledge Base Via External Natural Questions Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Previous methods on knowledge base question generation (KBQG) primarily focus on enhancing the quality of a single generated question. Recognizing the remarkable paraphrasing … |
Shasha Guo; Jing Zhang; Xirui Ke; Cuiping Li; Hong Chen; | ArXiv | 2023-09-23 |
673 | Furthest Reasoning with Plan Assessment: Stable Reasoning Path with Retrieval-Augmented Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These inaccuracies, accumulated by the iterative interaction between IR and LLM, lead to a disaster in effectiveness at the end. To overcome above barriers, in this paper, we propose a novel pipeline for MHQA called Furthest-Reasoning-with-Plan-Assessment (FuRePA), including an improved framework (Furthest Reasoning) and an attached module (Plan Assessor). |
Yin Zhu; Zhiling Luo; Gong Cheng; | arxiv-cs.CL | 2023-09-22 |
674 | HRoT: Hybrid Prompt Strategy and Retrieval of Thought for Table-Text Hybrid Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a new prompting strategy called Hybrid prompt strategy and Retrieval of Thought for TextTableQA. |
TONGXU LUO et. al. | arxiv-cs.CL | 2023-09-22 |
675 | SQUARE: Automatic Question Answering Evaluation Using Multiple Positive and Negative References Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new evaluation metric: SQuArE (Sentence-level QUestion AnsweRing Evaluation), using multiple reference answers (combining multiple correct and incorrect references) for sentence-form QA. |
Matteo Gabburo; Siddhant Garg; Rik Koncel Kedziorski; Alessandro Moschitti; | arxiv-cs.CL | 2023-09-21 |
676 | Retrieving Supporting Evidence for Generative Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we report two simple experiments to automatically validate generated answers against a corpus. |
Siqing Huo; Negar Arabzadeh; Charles L. A. Clarke; | arxiv-cs.IR | 2023-09-20 |
677 | Knowledge Graph Question Answering for Materials Science (KGQA4MAT): Developing Natural Language Interface for Metal-Organic Frameworks Knowledge Graph (MOF-KG) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a comprehensive benchmark dataset for Knowledge Graph Question Answering in Materials Science (KGQA4MAT), with a focus on metal-organic frameworks (MOFs). |
YUAN AN et. al. | arxiv-cs.AI | 2023-09-20 |
678 | Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we study the KG-augmented language model approach for solving the knowledge graph question answering (KGQA) task that requires rich world knowledge. |
YIKE WU et. al. | arxiv-cs.CL | 2023-09-20 |
679 | Visual Question Answering in The Medical Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present domain-specific pre-training strategies, including a novel contrastive learning pretraining method, to mitigate the problem of small datasets for the Med-VQA task. |
Louisa Canepa; Sonit Singh; Arcot Sowmya; | arxiv-cs.CV | 2023-09-20 |
680 | Enhancing Open-Domain Table Question Answering Via Syntax- and Structure-aware Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing studies of open-domain table QA either directly adopt text retrieval methods or consider the table structure only in the encoding layer for table retrieval, which may cause syntactical and structural information loss during table scoring. To address this issue, we propose a syntax- and structure-aware retrieval method for the open-domain table QA task. |
Nengzheng Jin; Dongfang Li; Junying Chen; Joanna Siebert; Qingcai Chen; | arxiv-cs.CL | 2023-09-19 |
681 | Localize, Retrieve and Fuse: A Generalized Framework for Free-Form Question Answering Over Tables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper proposes a generalized three-stage approach: Table-to- Graph conversion and cell localizing, external knowledge retrieval, and the fusion of table and text (called TAG-QA), to address the challenge of inferring long free-form answers in generative TableQA. |
WENTING ZHAO et. al. | arxiv-cs.CL | 2023-09-19 |
682 | Benchmarks for Pirá 2.0, A Reading Comprehension Dataset About The Ocean, The Brazilian Coast, and Climate Change Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we define six benchmarks over the Pir\’a dataset, covering closed generative question answering, machine reading comprehension, information retrieval, open question answering, answer triggering, and multiple choice question answering. |
PAULO PIROZELLI et. al. | arxiv-cs.CL | 2023-09-19 |
683 | QASnowball: An Iterative Bootstrapping Framework for High-Quality Question-Answering Data Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, obtaining sufficient data to build an effective and stable QA system still remains an open problem. For this problem, we introduce an iterative bootstrapping framework for QA data augmentation (named QASnowball), which can iteratively generate large-scale high-quality QA data based on a seed set of supervised examples. |
KUNLUN ZHU et. al. | arxiv-cs.CL | 2023-09-19 |
684 | KoBigBird-large: Transformation of Transformer for Korean Language Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents KoBigBird-large, a large size of Korean BigBird that achieves state-of-the-art performance and allows long sequence processing for Korean language understanding. |
KISU YANG et. al. | arxiv-cs.CL | 2023-09-19 |
685 | Syntax Tree Constrained Graph Network for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To fill the gap, we suggested a novel Syntax Tree Constrained Graph Network (STCGN) for VQA based on entity message passing and syntax tree. |
Xiangrui Su; Qi Zhang; Chongyang Shi; Jiachang Liu; Liang Hu; | arxiv-cs.CV | 2023-09-17 |
686 | NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper describes the NOWJ1 Team’s approach for the Automated Legal Question Answering Competition (ALQAC) 2023, which focuses on enhancing legal task performance by integrating classical statistical models and Pre-trained Language Models (PLMs). |
TAN-MINH NGUYEN et. al. | arxiv-cs.CL | 2023-09-16 |
687 | Multimodal Multi-Hop Question Answering Through A Conversation Between Tools and Efficiently Finetuned Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We employ a tool-interacting divide-and-conquer strategy enabling large language models (LLMs) to answer complex multimodal multi-hop questions. |
Hossein Rajabzadeh; Suyuchen Wang; Hyock Ju Kwon; Bang Liu; | arxiv-cs.CL | 2023-09-16 |
688 | PDFTriage: Question Answering Over Long, Structured Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: When a system has to query the document for context, this incongruity is brought to the fore, and seemingly trivial questions can trip up the QA system. To bridge this fundamental gap in handling structured documents, we propose an approach called PDFTriage that enables models to retrieve the context based on either structure or content. |
JON SAAD-FALCON et. al. | arxiv-cs.CL | 2023-09-16 |
689 | Silver Retriever: Advancing Neural Passage Retrieval for Polish Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present Silver Retriever, a neural retriever for Polish trained on a diverse collection of manually or weakly labeled datasets. |
Piotr Rybak; Maciej Ogrodniczuk; | arxiv-cs.CL | 2023-09-15 |
690 | D3: Data Diversity Design for Systematic Generalization in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present new evidence in the problem of Visual Question Answering (VQA) that reveals that the diversity of simple tasks (i.e. tasks formed by a few subtasks and concepts) plays a key role in achieving systematic generalization. |
AMIR RAHIMI et. al. | arxiv-cs.AI | 2023-09-15 |
691 | Investigating Answerability of LLMs for Long-Form Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a question-generation method from abstractive summaries and show that generating follow-up questions from summaries of long documents can create a challenging setting for LLMs to reason and infer from long contexts. |
Meghana Moorthy Bhat; Rui Meng; Ye Liu; Yingbo Zhou; Semih Yavuz; | arxiv-cs.CL | 2023-09-15 |
692 | SilverRetriever: Advancing Neural Passage Retrieval for Polish Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Modern open-domain question answering systems often rely on accurate and efficient retrieval components to find passages containing the facts necessary to answer the question. … |
Piotr Rybak; M. Ogrodniczuk; | ArXiv | 2023-09-15 |
693 | CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In recent years, large language models (LLMs) have shown remarkable capabilities at scale, particularly at generating text conditioned on a prompt. |
Rachneet Sachdeva; Martin Tutek; Iryna Gurevych; | arxiv-cs.CL | 2023-09-14 |
694 | Feature Engineering in Learning-to-Rank for Community Question Answering Task Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These data are leveraged in automated CQA ranking systems where similar questions (and answers) are presented in response to the query of the user. In this work, we empirically investigate a few aspects of this domain. |
Nafis Sajid; Md Rashidul Hasan; Muhammad Ibrahim; | arxiv-cs.LG | 2023-09-14 |
695 | Evaluating The Ebb and Flow: An In-depth Analysis of Question-Answering Trends Across Diverse Platforms Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Community Question Answering (CQA) platforms steadily gain popularity as they provide users with fast responses to their queries. The swiftness of these responses is contingent on … |
Rima Hazra; Agnik Saha; Somnath Banerjee; Animesh Mukherjee; | arxiv-cs.SI | 2023-09-12 |
696 | Answering Subjective Induction Questions on Products By Summarizing Multi-sources Multi-viewpoints Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: That is quite different from the traditional QA task, in which the answer to a factoid question is unique and can be found from a single data source. To address this new task, we propose a three-steps method. |
Yufeng Zhang; Meng-xiang Wang; Jianxing Yu; | arxiv-cs.CL | 2023-09-11 |
697 | NeCo@ALQAC 2023: Legal Domain Knowledge Acquisition for Low-Resource Languages Through Data Enrichment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents NeCo Team’s solutions to the Vietnamese text processing tasks provided in the Automated Legal Question Answering Competition 2023 (ALQAC 2023), focusing on legal domain knowledge acquisition for low-resource languages through data enrichment. |
HAI-LONG NGUYEN et. al. | arxiv-cs.CL | 2023-09-11 |
698 | AGent: A Novel Pipeline for Automatically Creating Unanswerable Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, manually annotating unanswerable questions is labor-intensive. To address this, we propose AGent, a novel pipeline that automatically creates new unanswerable questions by re-matching a question with a context that lacks the necessary information for a correct answer. |
Son Quoc Tran; Gia-Huy Do; Phong Nguyen-Thuan Do; Matt Kretchmar; Xinya Du; | arxiv-cs.CL | 2023-09-10 |
699 | Two Is Better Than One: Answering Complex Questions By Multiple Knowledge Sources with Generalized Links Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we formulate the novel Multi-KB-QA task that leverages the full and partial links among multiple KBs to derive correct answers, a benchmark with diversified link and query types is also constructed to efficiently evaluate Multi-KB-QA performance. |
MINHAO ZHANG et. al. | arxiv-cs.CL | 2023-09-10 |
700 | Duplicate Question Retrieval and Confirmation Time Prediction in Software Communities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To facilitate the task of the moderators, in this work, we have tackled two significant issues for the askubuntu CQA platform: (1) retrieval of duplicate questions given a new question and (2) duplicate question confirmation time prediction. |
Rima Hazra; Debanjan Saha; Amruit Sahoo; Somnath Banerjee; Animesh Mukherjee; | arxiv-cs.IR | 2023-09-10 |
701 | Code-Style In-Context Learning for Knowledge-Based Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, current powerful LLMs have little exposure to logic forms during pre-training, resulting in a high format error rate. To solve this problem, we propose a code-style in-context learning method for KBQA, which converts the generation process of unfamiliar logical form into the more familiar code generation process for LLMs. |
Zhijie Nie; Richong Zhang; Zhongyuan Wang; Xudong Liu; | arxiv-cs.CL | 2023-09-09 |
702 | MMHQA-ICL: Multimodal In-context Learning for Hybrid Question Answering Over Text, Tables and Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recently, with the rise of large language models (LLM), in-context learning (ICL) has become the most popular way to solve QA problems. We propose MMHQA-ICL framework for addressing this problems, which includes stronger heterogeneous data retriever and an image caption module. |
WEIHAO LIU et. al. | arxiv-cs.CL | 2023-09-09 |
703 | Can NLP Models ‘Identify’, ‘Distinguish’, and ‘Justify’ Questions That Don’t Have A Definitive Answer? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Can SOTA models accurately identify such questions and provide a reasonable response? To investigate the above question, we introduce QnotA, a dataset consisting of five different categories of questions that don’t have definitive answers. |
AYUSHI AGARWAL et. al. | arxiv-cs.CL | 2023-09-08 |
704 | Introducing Forecast Utterance for Conversational Data Science Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A significant challenge for the agent in this endeavor is to accurately comprehend the user’s prediction goals and, consequently, formulate precise ML tasks. In this paper, we take a pioneering step towards this ambitious goal by introducing a new concept called Forecast Utterance and then focus on the automatic and accurate interpretation of users’ prediction goals from these utterances. |
Md Mahadi Hassan; Alex Knipper; Shubhra Kanti Karmaker; | arxiv-cs.CL | 2023-09-07 |
705 | Interpretable Visual Question Answering Via Reasoning Supervision Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, such models are likely to disregard crucial visual cues and often rely on multimodal shortcuts and inherent biases of the language modality to predict the correct answer, a phenomenon commonly referred to as lack of visual grounding. In this work, we alleviate this shortcoming through a novel architecture for visual question answering that leverages common sense reasoning as a supervisory signal. |
Maria Parelli; Dimitrios Mallis; Markos Diomataris; Vassilis Pitsikalis; | arxiv-cs.CV | 2023-09-07 |
706 | Exploiting Language Models As A Source of Knowledge for Cognitive Agents Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Large language models (LLMs) provide capabilities far beyond sentence completion, including question answering, summarization, and natural-language inference. While many of these … |
James R. Kirk; Robert E. Wray; John E. Laird; | ArXiv | 2023-09-05 |
707 | Augmenting Black-box LLMs with Medical Textbooks for Clinical Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we present a system called LLMs Augmented with Medical Textbooks (LLM-AMT) designed to enhance the proficiency of LLMs in specialized domains. |
Yubo Wang; Xueguang Ma; Wenhu Chen; | arxiv-cs.CL | 2023-09-05 |
708 | ATM: Action Temporality Modeling for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce Action Temporality Modeling (ATM) for temporality reasoning via three-fold uniqueness: (1) rethinking the optical flow and realizing that optical flow is effective in capturing the long horizon temporality reasoning; (2) training the visual-text embedding by contrastive learning in an action-centric manner, leading to better action representations in both vision and text modalities; and (3) preventing the model from answering the question given the shuffled video in the fine-tuning stage, to avoid spurious correlation between appearance and motion and hence ensure faithful temporality reasoning. |
Junwen Chen; Jie Zhu; Yu Kong; | arxiv-cs.CV | 2023-09-05 |
709 | Understanding Video Scenes Through Text: Insights from Text-based Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The NewsVideoQA dataset contains question-answer pairs related to the text in news videos, while M4-ViteVQA comprises question-answer pairs from diverse categories like vlogging, traveling, and shopping. We provide an analysis of the formulation of these datasets on various levels, exploring the degree of visual understanding and multi-frame comprehension required for answering the questions. |
Soumya Jahagirdar; Minesh Mathew; Dimosthenis Karatzas; C. V. Jawahar; | arxiv-cs.CV | 2023-09-04 |
710 | MedChatZH: A Better Medical Adviser Learns from Better Instructions Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Generative large language models (LLMs) have shown great success in various applications, including question-answering (QA) and dialogue systems. However, in specialized domains … |
Yang Tan; Mingchen Li; Zijie Huang; Huiqun Yu; Guisheng Fan; | ArXiv | 2023-09-03 |
711 | Can I Trust Your Answer? Visually Grounded Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Experiments with different backbones demonstrate that this grounding mechanism improves both grounding and QA. With these efforts, we aim to push towards trustworthy VLMs in VQA systems. |
Junbin Xiao; Angela Yao; Yicong Li; Tat Seng Chua; | arxiv-cs.CV | 2023-09-03 |
712 | Generative Data Augmentation Using LLMs Improves Distributional Robustness in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We take a two-step generation approach, generating both contexts and QA pairs to augment existing datasets. |
Arijit Ghosh Chowdhury; Aman Chadha; | arxiv-cs.CL | 2023-09-02 |
713 | LeanContext: Cost-Efficient Domain-Specific Question Answering Using LLMs IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Question-answering (QA) is a significant application of Large Language Models (LLMs), shaping chatbot capabilities across healthcare, education, and customer service. However, … |
Md. Adnan Arefeen; Biplob K. Debnath; S. Chakradhar; | ArXiv | 2023-09-02 |
714 | Query Path Generation Via Bidirectional Reasoning for Multihop Question Answering From Knowledge Bases Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Multihop question answering from knowledge bases (KBQA) is a hot research topic in natural language processing. Recently, the graph neural network-based (GNN-based) methods have … |
GENG ZHANG et. al. | IEEE Transactions on Cognitive and Developmental Systems | 2023-09-01 |
715 | CLVIN: Complete Language-vision Interaction Network for Visual Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Chongqing Chen; Dezhi Han; Xiang Shen; | Knowl. Based Syst. | 2023-09-01 |
716 | Generative Retrieval for Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yongqing Li; Nan Yang; Liang Wang; Furu Wei; Wenjie Li; | Inf. Process. Manag. | 2023-09-01 |
717 | DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we describe the details of the training as well and the results on the different benchmarks. |
Shaltiel Shmidman; Avi Shmidman; Moshe Koppel; | arxiv-cs.CL | 2023-08-31 |
718 | Separate and Locate: Rethink The Text in Text-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The 1-D position embedding can only represent the left-right sequence relationship between words in a sentence, but not the complex spatial position relationship. To tackle these problems, we propose a novel method named Separate and Locate (SaL) that explores text contextual cues and designs spatial position embedding to construct spatial relations between OCR texts. |
Chengyang Fang; Jiangnan Li; Liang Li; Can Ma; Dayong Hu; | arxiv-cs.CV | 2023-08-30 |
719 | Hyperbolic Code Retrieval: A Novel Approach for Efficient Code Search Using Hyperbolic Space Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods often lead to computational and memory inefficiencies, posing a significant challenge to their real-world applicability. To tackle this challenge, we propose a novel approach, the Hyperbolic Code QA Matching (HyCoQA). |
XUNZHU TANG et. al. | arxiv-cs.SE | 2023-08-29 |
720 | KGConv, A Conversational Corpus Grounded in Wikidata Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present KGConv, a large, conversational corpus of 71k conversations where each question-answer pair is grounded in a Wikidata fact. |
Quentin Brabant; Gwenole Lecorve; Lina M. Rojas-Barahona; Claire Gardent; | arxiv-cs.CL | 2023-08-29 |
721 | Empowering Cross-lingual Abilities of Instruction-tuned Large Language Models By Translation-following Demonstrations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This disparity is demanded in further fine-tuning and affecting the cross-lingual abilities of LLMs. In this paper, we propose to empower Instructiontuned LLMs (It-LLMs) in languages other than English by building semantic alignment between them. |
Leonardo Ranaldi; Giulia Pucci; Andre Freitas; | arxiv-cs.CL | 2023-08-27 |
722 | Knowledge-Based Version Incompatibility Detection for Deep Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Therefore, these techniques cannot detect version issues due to undocumented version constraints or issues involving hardware drivers or OS. To address this challenge, we propose to leverage the abundant discussions of DL version issues from Stack Overflow to facilitate version incompatibility detection. |
Zhongkai Zhao; Bonan Kou; Mohamed Yilmaz Ibrahim; Muhao Chen; Tianyi Zhang; | arxiv-cs.SE | 2023-08-25 |
723 | Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Even so, suffering from hallucinations and the inability to access external knowledge, LLMs often come with incorrect or unfaithful intermediate reasoning steps, especially in the context of answering knowledge-intensive tasks such as KBQA. To alleviate this issue, we propose a framework called Knowledge-Driven Chain-of-Thought (KD-CoT) to verify and modify reasoning traces in CoT via interaction with external knowledge, and thus overcome the hallucinations and error propagation. |
KEHENG WANG et. al. | arxiv-cs.CL | 2023-08-25 |
724 | Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce the Qwen-VL series, a set of large-scale vision-language models (LVLMs) designed to perceive and understand both texts and images. |
JINZE BAI et. al. | arxiv-cs.CV | 2023-08-24 |
725 | SQuAD-SRC: A Dataset for Multi-Accent Spoken Reading Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we construct a large-scale multi-accent human spoken dataset SQuAD-SRC, in order to study the problem of multi-accent spoken reading comprehension. |
Yixuan Tang; Anthony K.H: Tung; | ijcai | 2023-08-23 |
726 | Answer Mining from A Pool of Images: Towards Retrieval-Based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Towards solving the RETVQA task, we propose a unified Multi Image BART (MI-BART) that takes a question and retrieved images using our relevance encoder for free-form fluent answer generation. |
Abhirama Subramanyam Penamakuri; Manish Gupta; Mithun Das Gupta; Anand Mishra; | ijcai | 2023-08-23 |
727 | Keep Skills in Mind: Understanding and Implementing Skills in Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a new approach named Dynamic Skill-aware Commonsense Question Answering (DSCQA), which transcends the limitations of traditional methods by informing the model about the need for each skill in questions and utilizes skills as a critical driver in CQA process. |
MEIKAI BAO et. al. | ijcai | 2023-08-23 |
728 | COOL, A Context Outlooker, and Its Application to Question Answering and Other Natural Language Processing Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present an outlook attention mechanism, COOL, for natural language processing. |
Fangyi Zhu; See-Kiong Ng; Stéphane Bressan; | ijcai | 2023-08-23 |
729 | FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To mitigate the burden associated with manual annotation, we introduce FlexKBQA by utilizing Large Language Models (LLMs) as program translators for addressing the challenges inherent in the few-shot KBQA task. |
ZHENYU LI et. al. | arxiv-cs.CL | 2023-08-23 |
730 | TG-VQA: Ternary Game of Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we innovatively resort to game theory, which can simulate complicated relationships among multiple players with specific interaction strategies, e.g., video, question, and answer as ternary players, to achieve fine-grained alignment for VideoQA task. |
HAO LI et. al. | ijcai | 2023-08-23 |
731 | Local and Global: Temporal Question Answering Via Information Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the fruitful efforts of previous models in temporal KGQA, they still have several limitations. (I) They neither emphasize the graph structural information between entities in KGs nor explicitly utilize a multi-hop relation path through graph neural networks to enhance answer prediction. (II) They adopt pre-trained language models (LMs) to obtain question representations, focusing merely on the global information related to the question while not highlighting the local information of the entities in KGs. To address these limitations, we introduce a novel model that simultaneously explores both Local information and Global information for the task of temporal KGQA (LGQA). |
YONGHAO LIU et. al. | ijcai | 2023-08-23 |
732 | A Logic-based Approach to Contrastive Explainability for Neurosymbolic Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a CE framework for VQA that uses a neurosymbolic VQA architecture which disentangles perception from reasoning. |
Thomas Eiter; Tobias Geibinger; Nelson Higuera; Johannes Oetsch; | ijcai | 2023-08-23 |
733 | Bridging The Gap: Deciphering Tabular Data Using Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the realm of natural language processing, the understanding of tabular data has perpetually stood as a focal point of scholarly inquiry. The emergence of expansive language models, exemplified by the likes of ChatGPT, has ushered in a wave of endeavors wherein researchers aim to harness these models for tasks related to table-based question answering. |
Hengyuan Zhang; Peng Chang; Zongcheng Ji; | arxiv-cs.CL | 2023-08-22 |
734 | Knowledge Graph Prompting for Multi-Document Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, few works explore this paradigm in the scenario of multi-document question answering (MD-QA), a task demanding a thorough understanding of the logical associations among the contents and structures of different documents. To fill this crucial gap, we propose a Knowledge Graph Prompting (KGP) method to formulate the right context in prompting LLMs for MD-QA, which consists of a graph construction module and a graph traversal module. |
YU WANG et. al. | arxiv-cs.CL | 2023-08-22 |
735 | HopPG: Self-Iterative Program Generation for Multi-Hop Question Answering Over Heterogeneous Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, this way ignores the semantic information of the intermediate answers at each hop, which is beneficial for subsequent generation. To alleviate these challenges, we propose a self-iterative framework for multi-hop program generation (HopPG) over heterogeneous knowledge, which leverages the previous execution results to retrieve supporting facts and generate subsequent programs hop by hop. |
Yingyao Wang; Yongwei Zhou; Chaoqun Duan; Junwei Bao; Tiejun Zhao; | arxiv-cs.CL | 2023-08-22 |
736 | Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Text-to-music generation (T2M-Gen) faces a major obstacle due to the scarcity of large-scale publicly available music datasets with natural language captions. To address this, we propose the Music Understanding LLaMA (MU-LLaMA), capable of answering music-related questions and generating captions for music files. |
Shansong Liu; Atin Sakkeer Hussain; Chenshuo Sun; Ying Shan; | arxiv-cs.SD | 2023-08-22 |
737 | DocPrompt: Large-scale Continue Pretrain for Zero-shot and Few-shot Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Docprompt for document question answering tasks with powerful zero-shot and few-shot performance. |
Sijin Wu; Dan Zhang; Teng Hu; Shikun Feng; | arxiv-cs.CL | 2023-08-21 |
738 | Generic Attention-model Explainability By Weighted Relevance Accumulation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a weighted relevancy strategy, which takes the importance of token values into consideration, to reduce distortion when equally accumulating relevance. |
Yiming Huang; Aozhe Jia; Xiaodan Zhang; Jiawei Zhang; | arxiv-cs.CV | 2023-08-20 |
739 | LibriSQA: A Novel Dataset and Framework for Spoken Question Answering with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Given the evident paucity of existing speech-text LLMs, we propose a lightweight, end-to-end framework to execute the SQA task on the LibriSQA, witnessing significant results. |
Zihan Zhao; Yiyang Jiang; Heyang Liu; Yanfeng Wang; Yu Wang; | arxiv-cs.CL | 2023-08-20 |
740 | Breaking Language Barriers: A Question Answering Dataset for Hindi and Marathi Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle the challenge of data scarcity, we have developed a novel approach for translating the SQuAD 2.0 dataset into Hindi and Marathi. |
Maithili Sabane; Onkar Litake; Aman Chadha; | arxiv-cs.CL | 2023-08-18 |
741 | Accelerated Materials Language Processing Enabled By GPT Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we develop generative pretrained transformer (GPT)-enabled pipelines where the complex architectures of prior MLP models are replaced with strategic designs of prompt engineering. |
Jaewoong Choi; Byungju Lee; | arxiv-cs.CL | 2023-08-18 |
742 | PUMGPT: A Large Vision-Language Model for Product Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the product understanding task, which plays an essential role in enhancing online shopping experience. |
SHUHUI WU et. al. | arxiv-cs.CV | 2023-08-18 |
743 | End-to-End Beam Retrieval for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce Beam Retrieval, an end-to-end beam retrieval framework for multi-hop QA. |
Jiahao Zhang; Haiyang Zhang; Dongmei Zhang; Yong Liu; Shen Huang; | arxiv-cs.CL | 2023-08-17 |
744 | Learning The Meanings of Function Words from Grounded Language Using A Visual Question Answering Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Yet recent neural-network based visual question answering models apparently can learn to use function words as part of answering questions about complex visual scenes. In this paper, we study what these models learn about function words, in the hope of better understanding how the meanings of these words can be learnt by both models and children. |
Eva Portelance; Michael C. Frank; Dan Jurafsky; | arxiv-cs.CL | 2023-08-16 |
745 | Answering Ambiguous Questions with A Database of Questions, Answers, and Revisions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a new state-of-the-art for answering ambiguous questions that exploits a database of unambiguous questions generated from Wikipedia. |
Haitian Sun; William W. Cohen; Ruslan Salakhutdinov; | arxiv-cs.CL | 2023-08-16 |
746 | An Ensemble Approach to Question Classification: Integrating Electra Transformer, GloVe, and LSTM Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study presents an innovative ensemble approach for question classification, combining the strengths of Electra, GloVe, and LSTM models. |
Sanad Aburass; Osama Dorgham; Maha Abu Rumman; | arxiv-cs.CL | 2023-08-13 |
747 | Performance Prediction for Multi-hop Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The problem is challenging due to the multi-step nature of the retrieval process, potential dependency of the steps and the reasoning involved. To tackle this challenge, we propose multHP, a novel pre-retrieval method for predicting the performance of open-domain multi-hop questions. |
Mohammadreza Samadi; Davood Rafiei; | arxiv-cs.CL | 2023-08-11 |
748 | Progressive Spatio-temporal Perception for Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a Progressive Spatio-Temporal Perception Network (PSTP-Net), which contains three modules that progressively identify key spatio-temporal regions w.r.t. questions. |
Guangyao Li; Wenxuan Hou; Di Hu; | arxiv-cs.CV | 2023-08-10 |
749 | Answering Unseen Questions With Smaller Language Models Using Rationale Generation and Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: When provided with sufficient explanatory context, smaller Language Models have been shown to exhibit strong reasoning ability on challenging short-answer question-answering tasks where the questions are unseen in training. We evaluate two methods for further improvement in this setting. |
Tim Hartill; Diana Benavides-Prado; Michael Witbrock; Patricia J. Riddle; | arxiv-cs.CL | 2023-08-09 |
750 | ADMUS: A Progressive Question Answering Framework Adaptable to Multiple Knowledge Sources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, we present ADMUS, a progressive knowledge base question answering framework designed to accommodate a wide variety of datasets, including multiple languages, diverse backbone knowledge bases, and disparate question answering datasets. To accomplish the purpose, we decouple the architecture of conventional KBQA systems and propose this dataset-independent framework. |
Yirui Zhan; Yanzeng Li; Minhao Zhang; Lei Zou; | arxiv-cs.CL | 2023-08-09 |
751 | Building Interpretable and Reliable Open Information Retriever for New Domains Overnight Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose an information retrieval pipeline that uses entity/event linking model and query decomposition model to focus more accurately on different information units of the query. |
Xiaodong Yu; Ben Zhou; Dan Roth; | arxiv-cs.CL | 2023-08-09 |
752 | Sci-CoT: Leveraging Large Language Models for Enhanced Knowledge Distillation in Small Models for Scientific QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the possibility of transferring the reasoning capabilities of LLMs to smaller models via knowledge distillation. |
Yuhan Ma; Haiqi Jiang; Chenyou Fan; | arxiv-cs.CL | 2023-08-08 |
753 | Top K Relevant Passage Retrieval for Biomedical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we work on the existing DPR framework for the biomedical domain and retrieve answers from the Pubmed articles which is a reliable source to answer medical questions. |
Shashank Gupta; | arxiv-cs.CL | 2023-08-08 |
754 | On Monotonic Aggregation for Open-domain QA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We identify the cause, and based on that we propose Judge-Specialist framework. |
Sang-eun Han; Yeonseok Jeong; Seung-won Hwang; Kyungjae Lee; | arxiv-cs.CL | 2023-08-08 |
755 | Towards An AI to Win Ghana’s National Science and Maths Quiz Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: That is the question we seek to answer in the NSMQ AI project, an open-source project that is building AI to compete live in the NSMQ and win. |
GEORGE BOATENG et. al. | arxiv-cs.HC | 2023-08-08 |
756 | SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present SciGraphQA, a synthetic multi-turn question-answer dataset related to academic graphs. |
Shengzhi Li; Nima Tajbakhsh; | arxiv-cs.CL | 2023-08-07 |
757 | KITLM: Domain-Specific Knowledge InTegration Into Language Models for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To boost the domain-specific understanding, we propose, KITLM, a novel knowledge base integration approach into language model through relevant information infusion. |
Ankush Agarwal; Sakharam Gawade; Amar Prakash Azad; Pushpak Bhattacharyya; | arxiv-cs.CL | 2023-08-07 |
758 | Trusting Language Models in Education Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our work, we propose to use an XGBoost on top of BERT to output the corrected probabilities, using features based on the attention mechanism. |
JOGI SUDA NETO et. al. | arxiv-cs.CL | 2023-08-07 |
759 | Prompt Guided Copy Mechanism for Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a pluggable approach for extractive methods that introduces a novel prompt-guided copy mechanism to improve the fluency and appropriateness of the extracted answers. |
YONG ZHANG et. al. | arxiv-cs.CL | 2023-08-07 |
760 | Redundancy-aware Transformer for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel transformer-based architecture, that aims to model VideoQA in a redundancy-aware manner. |
YICONG LI et. al. | arxiv-cs.CV | 2023-08-06 |
761 | PaniniQA: Enhancing Patient Education Through Interactive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present PaniniQA, a patient-centric interactive question answering system designed to help patients understand their discharge instructions. |
PENGSHAN CAI et. al. | arxiv-cs.CL | 2023-08-06 |
762 | A Criterion for Artificial General Intelligence: Hypothetic-deductive Reasoning, Tested on ChatGPT Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: An elementary proxy of hypothetic-deductive reasoning is causal reasoning. We propose simple tests for both types of reasoning, and apply them to ChatGPT. |
Louis Vervoort; Vitaliy Mizyakov; Anastasia Ugleva; | arxiv-cs.AI | 2023-08-05 |
763 | Decision Knowledge Graphs: Construction of and Usage in Question Answering for Clinical Practice Guidelines Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a Decision Knowledge Graph (DKG) representation to store CPGs and to perform question-answering on CPGs. |
Vasudhan Varma Kandula; Pushpak Bhattacharyya; | arxiv-cs.IR | 2023-08-05 |
764 | Learning to Select The Relevant History Turns in Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Irrelevant context, on the other hand, brings noise to the system, thereby resulting in a decline in the model’s performance. In this paper, we propose a framework, DHS-ConvQA (Dynamic History Selection in Conversational Question Answering), that first generates the context and question entities for all the history turns, which are then pruned on the basis of similarity they share in common with the question at hand. |
MUNAZZA ZAIB et. al. | arxiv-cs.CL | 2023-08-04 |
765 | WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present WebGLM, a web-enhanced question-answering system based on the General Language Model (GLM). |
XIAO LIU et. al. | kdd | 2023-08-04 |
766 | Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To contribute to automating the medical vision-language model, we propose a novel Chest-Xray Different Visual Question Answering (VQA) task. |
XINYUE HU et. al. | kdd | 2023-08-04 |
767 | RealCQA: Scientific Chart Question Answering As A Test-bed for First-Order Logic Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a comprehensive study of chart visual question-answering(QA) task, to address the challenges faced in comprehending and extracting data from chart visualizations within documents. |
Saleem Ahmed; Bhavin Jawade; Shubham Pandey; Srirangaraj Setlur; Venu Govindaraju; | arxiv-cs.CV | 2023-08-03 |
768 | Teaching Smaller Language Models To Generalise To Unseen Compositional Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To do so we propose a combination of multitask supervised pretraining on up to 93 tasks designed to instill diverse reasoning abilities, and a dense retrieval system that aims to retrieve a set of evidential paragraph fragments. |
Tim Hartill; Neset Tan; Michael Witbrock; Patricia J. Riddle; | arxiv-cs.CL | 2023-08-02 |
769 | Designing A Communication Bridge Between Communities: Participatory Design for A Question-Answering AI Agent Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: How do we design an AI system that is intended to act as a communication bridge between two user communities with different mental models and vocabularies? |
Jeonghyun Lee; Vrinda Nandan; Harshvardhan Sikka; Spencer Rugaber; Ashok Gole; | arxiv-cs.HC | 2023-08-01 |
770 | Neural Age Screening on Question Answering Communities Related Papers Related Patents Related Grants Related Venues Related Experts View |
Mohan Timilsina; A. Figueroa; | Eng. Appl. Artif. Intell. | 2023-08-01 |
771 | Counting-based Visual Question Answering with Serial Cascaded Attention Deep Learning Related Papers Related Patents Related Grants Related Venues Related Experts View |
Tesfayee Meshu Welde; L. Liao; | Pattern Recognit. | 2023-08-01 |
772 | Improving Visual Question Answering for Remote Sensing Via Alternate-guided Attention and Combined Loss Related Papers Related Patents Related Grants Related Venues Related Experts View |
JIANGFAN FENG et. al. | Int. J. Appl. Earth Obs. Geoinformation | 2023-08-01 |
773 | Improved Relation Span Detection in Question Answering Systems Over Extracted Knowledge Bases Related Papers Related Patents Related Grants Related Venues Related Experts View |
Somayyeh Behmanesh; Alireza Talebpour; M. Shamsfard; Mohammad Jafari; | Expert Syst. Appl. | 2023-08-01 |
774 | Olio: A Semantic Search Interface for Data Repositories Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For example, searching for a flight status or a game score returns a dynamically generated response along with supporting, pre-authored documents contextually relevant to the query. In this paper, we extend this hybrid search paradigm to data repositories that contain curated data sources and visualization content. |
Vidya Setlur; Andriy Kanyuka; Arjun Srinivasan; | arxiv-cs.HC | 2023-07-31 |
775 | KoBBQ: Korean Bias Benchmark for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present KoBBQ, a Korean bias benchmark dataset, and we propose a general framework that addresses considerations for cultural adaptation of a dataset. |
JIHO JIN et. al. | arxiv-cs.CL | 2023-07-31 |
776 | AMOE: A Tool to Automatically Extract and Assess Organizational Evidence for Continuous Cloud Audit Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an approach to facilitate the automatic assessment of organizational evidence, such as that extracted from security policy documents. |
Franz Deimling; Michela Fazzolari; | arxiv-cs.CR | 2023-07-31 |
777 | No That’s Not What I Meant: Handling Third Position Repair in Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: For stand-alone TPR execution, we perform both automatic and human evaluations on a fine-tuned T5 model, as well as OpenAI’s GPT-3 LLMs. |
Vevake Balaraman; Arash Eshghi; Ioannis Konstas; Ioannis Papaioannou; | arxiv-cs.CL | 2023-07-31 |
778 | Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we investigate the performance of instruction-following models across three information-seeking QA tasks. |
Vaibhav Adlakha; Parishad BehnamGhader; Xing Han Lu; Nicholas Meade; Siva Reddy; | arxiv-cs.CL | 2023-07-31 |
779 | Around The GLOBE: Numerical Aggregation Question-Answering on Heterogeneous Genealogical Knowledge Graphs with Deep Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Numerical aggregation QA is critical for distant reading and analysis for researchers (and the general public) interested in investigating cultural heritage domains. Therefore, in this study, we present a new end-to-end methodology for numerical aggregation QA for genealogical trees that includes: 1) an automatic method for training dataset generation; 2) a transformer-based table selection method, and 3) an optimized transformer-based numerical aggregation QA model. |
Omri Suissa; Maayan Zhitomirsky-Geffet; Avshalom Elmalech; | arxiv-cs.CL | 2023-07-30 |
780 | Question Answering with Deep Neural Networks for Semi-Structured Heterogeneous Genealogical Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, these supervised DNN models require training datasets that are absent in the genealogical domain. This study proposes an end-to-end approach for question answering using genealogical family trees by: 1) representing genealogical data as knowledge graphs, 2) converting them to texts, 3) combining them with unstructured texts, and 4) training a trans-former-based question answering model. |
Omri Suissa; Maayan Zhitomirsky-Geffet; Avshalom Elmalech; | arxiv-cs.CL | 2023-07-30 |
781 | BARTPhoBEiT: Pre-trained Sequence-to-Sequence and Image Transformers Models for Vietnamese Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there is a lack of models that target specific countries such as Vietnam. To address this limitation, we introduce a transformer-based Vietnamese model named BARTPhoBEiT. |
Khiem Vinh Tran; Kiet Van Nguyen; Ngan Luu Thuy Nguyen; | arxiv-cs.CL | 2023-07-28 |
782 | Context-VQA: Towards Context-Aware and Purposeful Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To further motivate and analyze the distinction between different contexts, we introduce Context-VQA, a VQA dataset that pairs images with contexts, specifically types of websites (e.g., a shopping website). |
Nandita Naik; Christopher Potts; Elisa Kreiss; | arxiv-cs.CL | 2023-07-28 |
783 | LOIS: Looking Out of Instance Semantics for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a finer model framework without bounding boxes in this work, termed Looking Out of Instance Semantics (LOIS) to tackle this important issue. |
SIYU ZHANG et. al. | arxiv-cs.CV | 2023-07-26 |
784 | Contributions to The Improvement of Question Answering Systems in The Biomedical Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: QA aims at providing inquirers with direct, short and precise answers to their natural language questions. In this Ph.D. thesis, we propose four contributions to improve the performance of QA in the biomedical domain. |
Mourad Sarrouti; | arxiv-cs.CL | 2023-07-25 |
785 | One Stop Shop for Question-Answering Dataset Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we offer a new visualization tool — Dataset Statistical View (DSV), to lower the barrier of research entry by providing easy access to the question-answering (QA) datasets that researchers can build their work upon. |
Chang Nian Chuy; Qinmin Vivian Hu; Chen Ding; | sigir | 2023-07-25 |
786 | Explainable Conversational Question Answering Over Heterogeneous Sources Via Iterative Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our method EXPLAIGNN overcomes these limitations by integrating information from a mixture of sources with user-comprehensible explanations for answers. |
Philipp Christmann; Rishiraj Saha Roy; Gerhard Weikum; | sigir | 2023-07-25 |
787 | Cross-Market Product-Related Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We conduct a data analysis to understand the scope of the cross-market question-answering task. |
NEGIN GHASEMI et. al. | sigir | 2023-07-25 |
788 | Learning to Ask Questions for Zero-shot Dialogue State Tracking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a method for performing zero-shot Dialogue State Tracking (DST) by casting the task as a learning-to-ask-questions framework. |
Diogo Tavares; David Semedo; Alexander Rudnicky; Joao Magalhaes; | sigir | 2023-07-25 |
789 | MAMO: Fine-Grained Vision-Language Representations Learning with Masked Multimodal Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a jointly masked multimodal modeling method to learn fine-grained multimodal representations. |
ZIJIA ZHAO et. al. | sigir | 2023-07-25 |
790 | On Answer Position Bias in Transformers for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we analyze the self-attention and embedding generation components of five Transformer-based models with different architectures and position embedding strategies. |
Rafael Glater; Rodrygo L. T. Santos; | sigir | 2023-07-25 |
791 | Keyword-Aware Relative Spatio-Temporal Graph Networks for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Keyword-aware Relative Spatio-Temporal (KRST) graph network for VideoQA. |
YI CHENG et. al. | arxiv-cs.CV | 2023-07-25 |
792 | A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a new pipeline for KI-VQA tasks, consisting of a retriever and a reader. |
Alireza Salemi; Juan Altmayer Pizzorno; Hamed Zamani; | sigir | 2023-07-25 |
793 | MythQA: Query-Based Large-Scale Check-Worthy Claim Detection Through Multi-Answer Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Many efforts have been put into how to identify check-worthy claims from a small scale of pre-collected claims, but how to efficiently detect check-worthy claims directly from a large-scale information source, such as Twitter, remains underexplored. To fill this gap, we introduce MythQA, a new multi-answer open-domain question answering(QA) task that involves contradictory stance mining for query-based large-scale check-worthy claim detection. |
Yang Bai; Anthony Colas; Daisy Zhe Wang; | sigir | 2023-07-25 |
794 | GPT-3 Models Are Few-Shot Financial Reasoners Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We run several experiments with GPT-3 and find that a separate retrieval model and logic engine continue to be essential components to achieving SOTA performance in this task, particularly due to the precise nature of financial questions and the complex information stored in financial documents. With this understanding, our refined prompt-engineering approach on GPT-3 achieves near SOTA accuracy without any fine-tuning. |
Raul Salles de Padua; Imran Qureshi; Mustafa U. Karakaplan; | arxiv-cs.CL | 2023-07-25 |
795 | Leader-Generator Net: Dividing Skill and Implicitness for Conquering FairytaleQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, a simple but effective Leader-Generator Network is proposed to explicitly separate and extract fine-grained reading skills and the implicitness or explicitness of the question. |
Wei Peng; Wanshui Li; Yue Hu; | sigir | 2023-07-25 |
796 | Limitations of Open-Domain Question Answering Benchmarks for Document-level Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this approach ignores important document-level cues that can be crucial in answering questions. This paper reviews three open-domain QA benchmarks from a document-level perspective and finds that they are biased towards passage-level information. |
Ehsan Kamalloo; Charles L. A. Clarke; Davood Rafiei; | sigir | 2023-07-25 |
797 | MA-MRC: A Multi-answer Machine Reading Comprehension Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim to construct an MRC dataset with both data of single answer and multiple answers. |
ZHIANG YUE et. al. | sigir | 2023-07-25 |
798 | BeamQA: Multi-hop Knowledge Graph Question Answering with Sequence-to-Sequence Prediction and Beam Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing KGQA frameworks that use such techniques often depend on learning a transformation from the query representation to the graph embedding space, which requires access to a large training dataset. We present BeamQA, an approach that overcomes these limitations by combining a sequence-to-sequence prediction model with beam search execution in the embedding space. |
Farah Atif; Ola El Khatib; Djellel Difallah; | sigir | 2023-07-25 |
799 | DICE: A Dataset of Italian Crime Event News Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The contribution of the paper are: (1) the creation of a corpus of 10,395 crime news; (2) the annotation schema; (3) a dataset of 10,395 news with automatic annotations; (4) a preliminary manual annotation using the proposed schema of 1000 documents. |
Giovanni Bonisoli; Maria Pia Di Buono; Laura Po; Federica Rollo; | sigir | 2023-07-25 |
800 | LAPCA: Language-Agnostic Pretraining with Cross-Lingual Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While previous works used machine translation and iterative training, we present a novel approach to cross-lingual pretraining called LAPCA (language-agnostic pretraining with cross-lingual alignment). We train the LAPCA-LM model based on XLM-RoBERTa and łexa that significantly improves cross-lingual knowledge transfer for question answering and sentence retrieval on, e.g., XOR-TyDi and Mr. TyDi datasets, and in the zero-shot cross-lingual scenario performs on par with supervised methods, outperforming many of them on MKQA. |
Dmitry Abulkhanov; Nikita Sorokin; Sergey Nikolenko; Valentin Malykh; | sigir | 2023-07-25 |
801 | A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We evaluate four state-of-the-art instruction-tuned large language models (LLMs) — ChatGPT, Flan-T5 UL2, Tk-Instruct, and Alpaca — on a set of 13 real-world clinical and biomedical natural language processing (NLP) tasks in English, such as named-entity recognition (NER), question-answering (QA), relation extraction (RE), etc. |
Yanis Labrak; Mickael Rouvier; Richard Dufour; | arxiv-cs.CL | 2023-07-22 |
802 | Robust Visual Question Answering: Datasets, Methods, and Future Challenges Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In recent years, various datasets and debiasing methods have been proposed to evaluate and enhance the VQA robustness, respectively. |
JIE MA et. al. | arxiv-cs.CV | 2023-07-21 |
803 | Towards Ontologically Grounded and Language-Agnostic Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: What we suggest here is that by a reification of abstract objects and by acknowledging the ontological distinction between concepts and types, we arrive at an ontologically grounded and language-agnostic representation that can alleviate the difficulties in KG integration. |
Walid S. Saba; | arxiv-cs.AI | 2023-07-20 |
804 | Generator-Retriever-Generator Approach for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel approach called Generator-Retriever-Generator (GRG) that combines document retrieval techniques with a large language model (LLM), by first prompting the model to generate contextual documents based on a given question. |
Abdelrahman Abdallah; Adam Jatowt; | arxiv-cs.CL | 2023-07-20 |
805 | Investigating The Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we present an initial analysis of the factual knowledge boundaries of LLMs and how retrieval augmentation affects LLMs on open-domain QA. |
RUIYANG REN et. al. | arxiv-cs.CL | 2023-07-20 |
806 | Explaining Autonomous Driving Actions with Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To facilitate interpretability of decision-making in autonomous driving, we present a Visual Question Answering (VQA) framework, which explains driving actions with question-answering-based causal reasoning. |
Shahin Atakishiyev; Mohammad Salameh; Housam Babiker; Randy Goebel; | arxiv-cs.CV | 2023-07-19 |
807 | Towards A Performance Analysis on Pre-trained Visual Question Answering Models for Autonomous Driving Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This short paper presents a preliminary analysis of three popular Visual Question Answering (VQA) models, namely ViLBERT, ViLT, and LXMERT, in the context of answering questions relating to driving scenarios. |
Kaavya Rekanar; Ciarán Eising; Ganesh Sistu; Martin Hayes; | arxiv-cs.CV | 2023-07-18 |
808 | Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing analyses are done in small models far from the state of the art. To address this, we present a case study of circuit analysis in the 70B Chinchilla model, aiming to test the scalability of circuit analysis. |
TOM LIEBERUM et. al. | arxiv-cs.LG | 2023-07-18 |
809 | Traffic-Domain Video Question Answering with Automatic Captioning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a novel approach termed Traffic-domain Video Question Answering with Automatic Captioning (TRIVIA), which serves as a weak-supervision technique for infusing traffic-domain knowledge into large video-language models. |
Ehsan Qasemi; Jonathan M. Francis; Alessandro Oltramari; | arxiv-cs.CV | 2023-07-18 |
810 | PAT: Parallel Attention Transformer for Visual Question Answering in Vietnamese Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present in this paper a novel scheme for multimodal learning named the Parallel Attention mechanism. |
Nghia Hieu Nguyen; Kiet Van Nguyen; | arxiv-cs.CL | 2023-07-17 |
811 | DecompEval: Evaluating Generated Texts As Unsupervised Decomposed Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, existing metrics only provide an evaluation score for each dimension without revealing the evidence to interpret how this score is obtained. To deal with these challenges, we propose a simple yet effective metric called DecompEval. |
PEI KE et. al. | arxiv-cs.CL | 2023-07-13 |
812 | Prompt Generate Train (PGT): Few-shot Domain Adaption of Retrieval Augmented Generation Models for Open Book Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a framework – Prompt, Generate, Train (PGT) – to efficiently develop a generative question-answering model for open-book question-answering over a proprietary collection of text documents. |
C. S. Krishna; | arxiv-cs.LG | 2023-07-12 |
813 | Masked Vision and Language Pre-training with Unimodal and Multimodal Contrastive Losses for Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a novel self-supervised approach that learns unimodal and multimodal feature representations of input images and text using medical image caption datasets, by leveraging both unimodal and multimodal contrastive losses, along with masked language modeling and image text matching as pretraining objectives. |
Pengfei Li; Gang Liu; Jinlong He; Zixu Zhao; Shenjun Zhong; | arxiv-cs.CV | 2023-07-11 |
814 | CAT-ViL: Co-Attention Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose an end-to-end Transformer with the Co-Attention gaTed Vision-Language (CAT-ViL) embedding for VQLA in surgical scenarios, which does not require feature extraction through detection models. |
Long Bai; Mobarakol Islam; Hongliang Ren; | arxiv-cs.CV | 2023-07-11 |
815 | Overview of BioASQ 2023: The Eleventh BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This is an overview of the eleventh edition of the BioASQ challenge in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2023. BioASQ is a series of … |
ANASTASIOS NENTIDIS et. al. | arxiv-cs.CL | 2023-07-11 |
816 | Emu: Generative Pretraining in Multimodality IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context. |
QUAN SUN et. al. | arxiv-cs.CV | 2023-07-11 |
817 | BeaverTails: Towards Improved Safety Alignment of LLM Via A Human-Preference Dataset IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the BeaverTails dataset, aimed at fostering research on safety alignment in large language models (LLMs). |
JIAMING JI et. al. | arxiv-cs.CL | 2023-07-10 |
818 | Self-Adaptive Sampling for Efficient Video Question-Answering on Image–Text Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The source codes pertaining to the method proposed in this paper are publicly available at https://github.com/declare-lab/sas-vqa. |
Wei Han; Hui Chen; Min-Yen Kan; Soujanya Poria; | arxiv-cs.CV | 2023-07-09 |
819 | Answering Ambiguous Questions Via Iterative Prompting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present AmbigPrompt to address the imperfections of existing approaches to answering ambiguous questions. |
WEIWEI SUN et. al. | arxiv-cs.CL | 2023-07-08 |
820 | Evaluating Open-Domain Question Answering in The Era of Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we conduct a thorough analysis of various open-domain QA models, including LLMs, by manually evaluating their answers on a subset of NQ-open, a popular benchmark. |
Ehsan Kamalloo; Nouha Dziri; Charles Clarke; Davood Rafiei; | acl | 2023-07-08 |
821 | Content Moderation for Evolving Policies Using Binary Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose to model content moderation as a binary question answering problem where the questions validate the loosely coupled themes constituting a policy. |
SANKHA SUBHRA MULLICK et. al. | acl | 2023-07-08 |
822 | Tab-CQA: A Tabular Conversational Question Answering Dataset on Financial Reports Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Tab-CQA, a tabular CQA dataset created from Chinese financial reports that are extracted from listed companies in a wide range of different sectors in the past 30 years. |
Chuang Liu; Junzhuo Li; Deyi Xiong; | acl | 2023-07-08 |
823 | (QA)2: Question Answering with Questionable Assumptions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose (QA)2 (Question Answering with Questionable Assumptions), an open-domain evaluation dataset consisting of naturally occurring search engine queries that may or may not contain questionable assumptions. |
Najoung Kim; Phu Mon Htut; Samuel R. Bowman; Jackson Petty; | acl | 2023-07-08 |
824 | Dynamic Heterogeneous-Graph Reasoning with Language Models and Knowledge Representation Learning for Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a dynamic heterogeneous-graph reasoning method with LMs and knowledge representation learning (DHLK), which constructs a heterogeneous knowledge graph (HKG) based on multiple knowledge sources and optimizes the structure and knowledge representation of the HKG using a two-stage pruning strategy and knowledge representation learning (KRL). |
Yujie Wang; Hu Zhang; Jiye Liang; Ru Li; | acl | 2023-07-08 |
825 | History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a History Semantic Graph Enhanced KBQA model (HSGE) that is able to effectively model long-range semantic dependencies in conversation history while maintaining low computational cost. |
HAO SUN et. al. | acl | 2023-07-08 |
826 | Long-Tailed Question Answering in An Open World Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we define Open Long-Tailed QA (OLTQA) as learning from long-tailed distributed data and optimizing performance over seen and unseen QA tasks. |
Yi Dai; Hao Lang; Yinhe Zheng; Fei Huang; Yongbin Li; | acl | 2023-07-08 |
827 | Accurate Training of Web-based Question Answering Systems with Feedback from Ranked Users Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first collect a large scale (16M) QA dataset with real feedback sampled from the QA traffic of a popular Virtual Assistant. Second, we use this data to develop two strategies for filtering unreliable users and thus de-noise feedback: (i) ranking users with an automatic classifier, and (ii) aggregating feedback over similar instances and comparing users between each other. |
Liang Wang; Ivano Lauriola; Alessandro Moschitti; | acl | 2023-07-08 |
828 | Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here, what to retrieve depends on what has already been derived, which in turn may depend on what was previously retrieved. To address this, we propose IRCoT, a new approach for multi-step QA that interleaves retrieval with steps (sentences) in a CoT, guiding the retrieval with CoT and in turn using retrieved results to improve CoT. |
Harsh Trivedi; Niranjan Balasubramanian; Tushar Khot; Ashish Sabharwal; | acl | 2023-07-08 |
829 | Knowledgeable Parameter Efficient Tuning Network for Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple knowledgeable parameter efficient tuning network to couple PLMs with external knowledge for commonsense question answering. |
Ziwang Zhao; Linmei Hu; Hanyu Zhao; Yingxia Shao; Yequan Wang; | acl | 2023-07-08 |
830 | Event Extraction As Question Generation and Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose QGA-EE, which enables a Question Generation (QG) model to generate questions that incorporate rich contextual information instead of using fixed templates. |
Di Lu; Shihao Ran; Joel Tetreault; Alejandro Jaimes; | acl | 2023-07-08 |
831 | What Is The Real Intention Behind This Question? Dataset Collection and Intention Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper is the first study to introduce a dataset (Question Intention Dataset) that includes questions with positive/neutral and negative intentions and the underlying intention categories within each group. |
Maryam Sadat Mirzaei; Kourosh Meshgi; Satoshi Sekine; | acl | 2023-07-08 |
832 | AlignScore: Evaluating Factual Consistency with A Unified Alignment Function IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose AlignScore, a new holistic metric that applies to a variety of factual inconsistency scenarios as above. |
Yuheng Zha; Yichi Yang; Ruichen Li; Zhiting Hu; | acl | 2023-07-08 |
833 | Towards Benchmarking and Improving The Temporal Reasoning Capability of Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a comprehensive probing dataset TempReason to evaluate the temporal reasoning capability of large language models. |
Qingyu Tan; Hwee Tou Ng; Lidong Bing; | acl | 2023-07-08 |
834 | FACTIFY-5WQA: 5W Aspect-based Fact Verification Through Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a 5W framework (who, what, when, where, and why) for question-answer-based fact explainability. |
ANKU RANI et. al. | acl | 2023-07-08 |
835 | Improving Pretraining Techniques for Code-Switched NLP Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore different masked language modeling (MLM) pretraining techniques for code-switched text that are cognizant of language boundaries prior to masking. |
Richeek Das; Sahasra Ranjan; Shreya Pathak; Preethi Jyothi; | acl | 2023-07-08 |
836 | MeetingQA: Extractive Question-Answering on Meeting Transcripts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, meeting discussions also have a useful question-answering (QA) component, crucial to understanding the discourse or meeting content, and can be used to build interactive interfaces on top of long transcripts. Hence, in this work, we leverage this inherent QA component of meeting discussions and introduce MeetingQA, an extractive QA dataset comprising of questions asked by meeting participants and corresponding responses. |
ARCHIKI PRASAD et. al. | acl | 2023-07-08 |
837 | Few-shot In-context Learning on Knowledge Base Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To handle questions over diverse KBQA datasets with a unified training-free framework, we propose KB-BINDER, which for the first time enables few-shot in-context learning over KBQA tasks. |
TIANLE LI et. al. | acl | 2023-07-08 |
838 | A Survey for Efficient Open Domain Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we will survey recent advancements in the efficiency of ODQA models and conclude core techniques for achieving efficiency. |
QIN ZHANG et. al. | acl | 2023-07-08 |
839 | Learning Answer Generation Using Supervision from Automatic Question Answering Evaluators Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel training paradigm for GenQA using supervision from automatic QA evaluation models (GAVA). |
Matteo Gabburo; Siddhant Garg; Rik Koncel-Kedziorski; Alessandro Moschitti; | acl | 2023-07-08 |
840 | Do I Have The Knowledge to Answer? Investigating Answerability of Knowledge Base Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We create GrailQAbility, a new benchmark KBQA dataset with unanswerability, by first identifying various forms of KB incompleteness that make questions unanswerable, and then systematically adapting GrailQA (a popular KBQA dataset with only answerable questions). |
MAYUR PATIDAR et. al. | acl | 2023-07-08 |
841 | Post-Abstention: Towards Reliably Re-Attempting The Abstained Instances in QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we present an explorative study on �Post-Abstention�, a task that allows re-attempting the abstained instances with the aim of increasing **coverage** of the system without significantly sacrificing its **accuracy**. |
Neeraj Varshney; Chitta Baral; | acl | 2023-07-08 |
842 | Few-shot Reranking for Multi-hop QA Via Language Model Prompting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To alleviate the need for a large number of labeled question-document pairs for retriever training, we propose PromptRank, which relies on language model prompting for multi-hop path reranking. |
Muhammad Khalifa; Lajanugen Logeswaran; Moontae Lee; Honglak Lee; Lu Wang; | acl | 2023-07-08 |
843 | XPQA: Cross-Lingual Product Question Answering in 12 Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While existing work on PQA focuses mainly on English, in practice there is need to support multiple customer languages while leveraging product information available in English. To study this practical industrial task, we present xPQA, a large-scale annotated cross-lingual PQA dataset in 12 languages, and report results in (1) candidate ranking, to select the best English candidate containing the information to answer a non-English question; and (2) answer generation, to generate a natural-sounding non-English answer based on the selected English candidate. |
Xiaoyu Shen; Akari Asai; Bill Byrne; Adria De Gispert; | acl | 2023-07-08 |
844 | BUCA: A Binary Classification Approach to Unsupervised Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to transform the downstream multiple choice question answering task into a simpler binary classification task by ranking all candidate answers according to their reasonableness. |
Jie He; Simon U; Victor Gutierrez-Basulto; Jeff Pan; | acl | 2023-07-08 |
845 | Zero-shot Cross-lingual Transfer With Learned Projections Using Unlabeled Target-Language Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We construct language-specific subspaces using standard linear algebra constructs and selectively project source-language representations into the target language subspace during task-specific finetuning using two schemes. |
Ujan Deb; Ridayesh Parab; Preethi Jyothi; | acl | 2023-07-08 |
846 | CREPE: Open-Domain Question Answering with False Presuppositions IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce CREPE, a QA dataset containing a natural distribution of presupposition failures from online information-seeking forums. |
Xinyan Yu; Sewon Min; Luke Zettlemoyer; Hannaneh Hajishirzi; | acl | 2023-07-08 |
847 | Context-Aware Transformer Pre-Training for Answer Sentence Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose three pre-training objectives designed to mimic the downstream fine-tuning task of contextual AS2. |
Luca Di Liello; Siddhant Garg; Alessandro Moschitti; | acl | 2023-07-08 |
848 | WebCPM: Interactive Web Search for Chinese Long-form Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce WebCPM, the first Chinese LFQA dataset. |
YUJIA QIN et. al. | acl | 2023-07-08 |
849 | IM-TQA: A Chinese Table Question Answering Dataset with Implicit and Multi-type Table Structures Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Correspondingly, we propose an RGCN-RCI framework outperforming recent baselines. |
MINGYU ZHENG et. al. | acl | 2023-07-08 |
850 | Multi-Source Test-Time Adaptation As Dueling Bandits for Extractive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we study multi-source test-time model adaptation from user feedback, where K distinct models are established for adaptation. |
Hai Ye; Qizhe Xie; Hwee Tou Ng; | acl | 2023-07-08 |
851 | WikiHowQA: A Comprehensive Benchmark for Multi-Document Non-Factoid Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: There is a critical need for high-quality resources for multi-document NFQA (MD-NFQA) to train new models and evaluate answers? grounding and factual consistency in relation to supporting documents. To address this gap, we introduce WikiHowQA, a new multi-document NFQA benchmark built on WikiHow, a website dedicated to answering ?how-to? questions. |
Valeriia Bolotova-Baranova; Vladislav Blinov; Sofya Filippova; Falk Scholer; Mark Sanderson; | acl | 2023-07-08 |
852 | Improving Knowledge Production Efficiency With Question Answering on Conversation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The challenges of conversation-based QA include: 1) answers may be scattered among multiple dialogue turns; 2) understanding complex dialogue contexts is more complicated than documents. To address these challenges, we propose a multi-span extraction model on this task and introduce continual pre-training and multi-task learning schemes to further improve model performance. |
CHANGLIN YANG et. al. | acl | 2023-07-08 |
853 | Won�t Get Fooled Again: Answering Questions with False Premises Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such frailties of PLMs often allude to the lack of knowledge within them. In this paper, we find that the PLMs already possess the knowledge required to rebut such questions, and the key is how to activate the knowledge. |
SHENGDING HU et. al. | acl | 2023-07-08 |
854 | Reasoning Over Hierarchical Question Decomposition Tree for Explainable Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to leverage question decomposing for heterogeneous knowledge integration, by breaking down a complex question into simpler ones, and selecting the appropriate knowledge source for each sub-question. |
JIAJIE ZHANG et. al. | acl | 2023-07-08 |
855 | Single Sequence Prediction Over Reasoning Graphs for Multi-hop QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While such models can lead to better interpretability and high quantitative scores, they often have difficulty accurately identifying the passages corresponding to key entities in the context, resulting in incorrect passage hops and a lack of faithfulness in the reasoning path. To address this, we propose a single-sequence prediction method over a local reasoning graph that integrates a graph structure connecting key entities in each context passage to relevant subsequent passages for each question. |
Gowtham Ramesh; Makesh Narsimhan Sreedhar; Junjie Hu; | acl | 2023-07-08 |
856 | RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our results indicate that both state-of-the-art Table QA models and large language models (e. g. , GPT-3) with few-shot learning falter in these adversarial sets. We propose to address this problem by using large language models to generate adversarial examples to enhance training, which significantly improves the robustness of Table QA models. |
YILUN ZHAO et. al. | acl | 2023-07-08 |
857 | WeCheck: Strong Factual Consistency Checker Via Weakly Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Bias in synthetic text or upstream tasks makes them perform poorly on text actually generated by language models, especially for general evaluation for various tasks. To alleviate this problem, we propose a weakly supervised framework named WeCheck that is directly trained on actual generated samples from language models with weakly annotated labels. |
WENHAO WU et. al. | acl | 2023-07-08 |
858 | MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, multi-table operations often result in a tabular output, which necessitates table generation capabilities of tabular QA models. To fill this gap, we propose a new task of answering questions over multiple tables. |
Vaishali Pal; Andrew Yates; Evangelos Kanoulas; Maarten de Rijke; | acl | 2023-07-08 |
859 | Query Refinement Prompts for Closed-Book Long-Form QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We resolve the difficulties to evaluate long-form output by doing both tasks at once ? to do question answering that requires long-form answers. Such questions tend to be multifaceted, i. e. , they may have ambiguities and/or require information from multiple sources. |
Reinald Kim Amplayo; Kellie Webster; Michael Collins; Dipanjan Das; Shashi Narayan; | acl | 2023-07-08 |
860 | SVIT: Scaling Up Visual Instruction Tuning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Thanks to the emerging of foundation models, the large language and vision models are integrated to acquire the multimodal ability of visual captioning, question answering, etc. |
Bo Zhao; Boya Wu; Muyang He; Tiejun Huang; | arxiv-cs.CV | 2023-07-08 |
861 | Multi-Row, Multi-Span Distant Supervision For Table+Text Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This leads to a noisy multi-instance training regime. We present MITQA, a transformer-based TextTableQA system that is explicitly designed to cope with distant supervision along both these axes, through a multi-instance loss objective, together with careful curriculum design. |
VISHWAJEET KUMAR et. al. | acl | 2023-07-08 |
862 | Elaboration-Generating Commonsense Question Answering at Scale Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In question answering requiring common sense, language models (e. g. , GPT-3) have been used to generate text expressing background knowledge that helps improve performance. Yet the cost of working with such models is very high; in this work, we finetune smaller language models to generate useful intermediate context, referred to here as elaborations. |
Wenya Wang; Vivek Srikumar; Hannaneh Hajishirzi; Noah A. Smith; | acl | 2023-07-08 |
863 | Reading Between The Lanes: Text VideoQA on The Road Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Systems that exploit such information to assist the driver should not only extract and incorporate visual and textual cues from the video stream but also reason over time. To address this issue, we introduce RoadTextVQA, a new dataset for the task of video question answering (VideoQA) in the context of driver assistance. |
George Tom; Minesh Mathew; Sergi Garcia; Dimosthenis Karatzas; C. V. Jawahar; | arxiv-cs.CV | 2023-07-08 |
864 | Modular Visual Question Answering Via Code Generation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a framework that formulates visual question answering as modular code generation. |
SANJAY SUBRAMANIAN et. al. | acl | 2023-07-08 |
865 | Concise Answers to Complex Questions: Summarization of Long-form Answers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Together, we present the first study on summarizing long-form answers, taking a step forward for QA agents that can provide answers at multiple granularities. |
Abhilash Potluri; Fangyuan Xu; Eunsol Choi; | acl | 2023-07-08 |
866 | Product Question Answering in E-Commerce: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim to systematically review existing research efforts on PQA. |
Yang Deng; Wenxuan Zhang; Qian Yu; Wai Lam; | acl | 2023-07-08 |
867 | Answering Unanswered Questions Through Semantic Reformulations in Spoken QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a Semantic Question Reformulation (SURF) model offering three linguistically-grounded operations (repair, syntactic reshaping, generalization) to rewrite questions to facilitate answering. |
Pedro Faustini; Zhiyu Chen; Besnik Fetahu; Oleg Rokhlenko; Shervin Malmasi; | acl | 2023-07-08 |
868 | Chain-of-Skills: A Configurable Model for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a modular retriever where individual modules correspond to key skills that can be reused across datasets. |
KAIXIN MA et. al. | acl | 2023-07-08 |
869 | Multi-granularity Temporal Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome the limitation, in this paper, we motivate the notion of multi-granularity temporal question answering over knowledge graphs and present a large scale dataset for multi-granularity TKGQA, namely MultiTQ. |
Ziyang Chen; Jinzhi Liao; Xiang Zhao; | acl | 2023-07-08 |
870 | Question-Answering in A Low-resourced Language: Benchmark Dataset and Models for Tigrinya Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents a native QA dataset for an East African language, Tigrinya. |
Fitsum Gaim; Wonsuk Yang; Hancheol Park; Jong Park; | acl | 2023-07-08 |
871 | LexSym: Compositionality As Lexical Symmetry Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a domain-general and model-agnostic formulation of compositionality as a constraint on symmetries of data distributions rather than models. |
Ekin Akyurek; Jacob Andreas; | acl | 2023-07-08 |
872 | Using Contradictions Improves Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work examines the use of contradiction in natural language inference (NLI) for question answering (QA). |
Etienne Fortier-Dubois; Domenic Rosati; | acl | 2023-07-08 |
873 | Peek Across: Improving Multi-Document Modeling Via Cross-Document Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The integration of multi-document pre-training objectives into language models has resulted in remarkable improvements in multi-document downstream tasks. In this work, we propose extending this idea by pre-training a generic multi-document model from a novel cross-document question answering pre-training objective. |
Avi Caciularu; Matthew Peters; Jacob Goldberger; Ido Dagan; Arman Cohan; | acl | 2023-07-08 |
874 | An Inner Table Retriever for Robust Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Inner Table Retriever (ITR), a general-purpose approach for handling long tables in TableQA that extracts sub-tables to preserve the most relevant information for a question. |
Weizhe Lin; Rexhina Blloshmi; Bill Byrne; Adria de Gispert; Gonzalo Iglesias; | acl | 2023-07-08 |
875 | Do Question Answering Modeling Improvements Hold Across Benchmarks? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Do question answering (QA) modeling improvements (e. g. , choice of architecture and training procedure) hold consistently across the diverse landscape of QA benchmarks? To study this question, we introduce the notion of concurrence�two benchmarks have high concurrence on a set of modeling approaches if they rank the modeling approaches similarly. |
Nelson F. Liu; Tony Lee; Robin Jia; Percy Liang; | acl | 2023-07-08 |
876 | Say What You Mean! Large Language Models Speak Too Positively About Negative Commonsense Knowledge IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work examines the ability of LLMs on negative commonsense knowledge. |
JIANGJIE CHEN et. al. | acl | 2023-07-08 |
877 | A Critical Evaluation of Evaluations for Long-form Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a careful analysis of experts� evaluation, which focuses on new aspects such as the comprehensiveness of the answer. |
Fangyuan Xu; Yixiao Song; Mohit Iyyer; Eunsol Choi; | acl | 2023-07-08 |
878 | Task-Aware Specialization for Efficient and Robust Dense Retrieval for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We thus propose a new architecture, Task-Aware Specialization for dEnse Retrieval (TASER), which enables parameter sharing by interleaving shared and specialized blocks in a single encoder. |
Hao Cheng; Hao Fang; Xiaodong Liu; Jianfeng Gao; | acl | 2023-07-08 |
879 | DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a new paradigm in which QA models are trained to disentangle the two sources of knowledge. |
ELLA NEEMAN et. al. | acl | 2023-07-08 |
880 | Faithful Question Answering with Monte-Carlo Planning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose FAME (FAithful question answering with MontE-carlo planning) to answer questions based on faithful reasoning steps. |
Ruixin Hong; Hongming Zhang; Hong Zhao; Dong Yu; Changshui Zhang; | acl | 2023-07-08 |
881 | To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work proposes a more realistic end-to-end domain shift evaluation setting covering five diverse domains. |
Dheeru Dua; Emma Strubell; Sameer Singh; Pat Verga; | acl | 2023-07-08 |
882 | TRAQ: Trustworthy Retrieval Augmented Question Answering Via Conformal Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Retrieval augmented generation (RAG) is a promising strategy to avoid hallucinations, but it does not provide guarantees on its correctness. To address this challenge, we propose the Trustworthy Retrieval Augmented Question Answering, or $\textit{TRAQ}$, which provides the first end-to-end statistical correctness guarantee for RAG. |
Shuo Li; Sangdon Park; Insup Lee; Osbert Bastani; | arxiv-cs.CL | 2023-07-06 |
883 | Enhancing Multiple-choice Question Answering Through Sequential Fine-tuning and Curriculum Learning Strategies Related Papers Related Patents Related Grants Related Venues Related Experts View |
Gulsum Yigit; M. Amasyali; | Knowledge and Information Systems | 2023-07-06 |
884 | UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our team approached Task 1 of the challenge by proposing a multimodal learning method with image enhancement to improve the VQA performance on gastrointestinal images. |
Triet M. Thai; Anh T. Vo; Hao K. Tieu; Linh N. P. Bui; Thien T. B. Nguyen; | arxiv-cs.CV | 2023-07-06 |
885 | VisKoP: Visual Knowledge Oriented Programming for Interactive Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present Visual Knowledge oriented Programming platform (VisKoP), a knowledge base question answering (KBQA) system that integrates human into the loop to edit and debug the knowledge base (KB) queries. |
ZIJUN YAO et. al. | arxiv-cs.CL | 2023-07-06 |
886 | CORE-GPT: Combining Open Access Research and Large Language Models for Credible, Trustworthy Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: In this paper, we present CORE-GPT, a novel question-answering platform that combines GPT-based language models and more than 32 million full-text open access scientific articles … |
David Pride; M. Cancellieri; Petr Knoth; | ArXiv | 2023-07-06 |
887 | Text Alignment Is An Efficient Unified Model for Massive NLP Tasks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose text alignment as an efficient unified model for a wide range of crucial tasks involving text entailment, similarity, question answering (and answerability), factual consistency, and so forth. |
Yuheng Zha; Yichi Yang; Ruichen Li; Zhiting Hu; | arxiv-cs.CL | 2023-07-05 |
888 | Won’t Get Fooled Again: Answering Questions with False Premises Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Pre-trained language models (PLMs) have shown unprecedented potential in various fields, especially as the backbones for question-answering (QA) systems. However, they tend to be … |
SHENGDING HU et. al. | Annual Meeting of the Association for Computational … | 2023-07-05 |
889 | Won’t Get Fooled Again: Answering Questions with False Premises Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Such frailties of PLMs often allude to the lack of knowledge within them. In this paper, we find that the PLMs already possess the knowledge required to rebut such questions, and the key is how to activate the knowledge. |
SHENGDING HU et. al. | arxiv-cs.CL | 2023-07-05 |
890 | Modeling Tag Prediction Based on Question Tagging Behavior Analysis of CommunityQA Platform Users Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To develop better tag prediction across diverse communities and domains, we performed a thorough analysis of users’ tagging behavior in 17 StackExchange communities. |
Kuntal Kumar Pal; Michael Gamon; Nirupama Chandrasekaran; Silviu Cucerzan; | arxiv-cs.CL | 2023-07-03 |
891 | Localized Questions in Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Consequently, VQA models are limited in their interpretability power and the possibility to probe the model about specific image regions. This paper proposes a novel approach for medical VQA that addresses this limitation by developing a model that can answer questions about image regions while considering the context necessary to answer the questions. |
Sergio Tascon-Morales; Pablo Márquez-Neila; Raphael Sznitman; | arxiv-cs.CV | 2023-07-03 |
892 | Memory-Aware Attentive Control for Community Question Answering With Knowledge-Based Dual Refinement Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The question answering system in open domain enables a machine to automatically select and generate the answer for questions posed by humans in a natural language form on the … |
Jinmeng Wu; Tingting Mu; Jeyarajan Thiyagalingam; J. Y. Goulermas; | IEEE Transactions on Systems, Man, and Cybernetics: Systems | 2023-07-01 |
893 | Biomedical Extractive Question Answering Based on Dynamic Routing and Answer Voting Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zhongjian Hu; Peng Yang; Bing Li; Yuankang Sun; Biao Yang; | Inf. Process. Manag. | 2023-07-01 |
894 | ReGR: Relation-aware Graph Reasoning Framework for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Z. Wang; Fangtao Li; K. Ota; M. Dong; Bin Wu; | Inf. Process. Manag. | 2023-07-01 |
895 | Counterfactual Can Be Strong in Medical Question and Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zhen Yang; Yongbin Liu; Chunping Ouyang; Lin Ren; Wen Wen; | Inf. Process. Manag. | 2023-07-01 |
896 | Spatial-Semantic Collaborative Graph Network for Textbook Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Textbook Question Answering (TQA) task requires answering questions by reasoning based on both the given diagrams and text context. There are mainly two challenges for the task. … |
YAXIAN WANG et. al. | IEEE Transactions on Circuits and Systems for Video … | 2023-07-01 |
897 | Hic-KGQA: Improving Multi-hop Question Answering Over Knowledge Graph Via Hypergraph and Inference Chain Related Papers Related Patents Related Grants Related Venues Related Experts View |
JINGCHAO WANG et. al. | Knowl. Based Syst. | 2023-07-01 |
898 | Encoder-decoder Cycle for Visual Question Answering Based on Perception-action Cycle Related Papers Related Patents Related Grants Related Venues Related Experts View |
Safaa Abdullahi Moallim Mohamud; Amin Jalali; Minho Lee; | Pattern Recognit. | 2023-07-01 |
899 | Single Sequence Prediction Over Reasoning Graphs for Multi-hop QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While such models can lead to better interpretability and high quantitative scores, they often have difficulty accurately identifying the passages corresponding to key entities in the context, resulting in incorrect passage hops and a lack of faithfulness in the reasoning path. To address this, we propose a single-sequence prediction method over a local reasoning graph (\model)\footnote{Code/Models will be released at \url{https://github.com/gowtham1997/SeqGraph}} that integrates a graph structure connecting key entities in each context passage to relevant subsequent passages for each question. |
Gowtham Ramesh; Makesh Sreedhar; Junjie Hu; | arxiv-cs.CL | 2023-07-01 |
900 | Multimodal Prompt Retrieval for Generative Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite the recent advances in VQA, existing methods mainly adopt a discriminative formulation that predicts answers within a pre-defined label set, leading to easy overfitting on low-resource domains with limited labeled data (e.g., medicine) and poor generalization under domain shift to another dataset. To tackle this limitation, we propose a novel generative model enhanced by multimodal prompt retrieval (MPR) that integrates retrieved prompts and multimodal features to generate answers in free text. |
Timothy Ossowski; Junjie Hu; | arxiv-cs.CV | 2023-06-30 |
901 | ELECTRA-based Graph Network Model for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Pengxuan Zhu; Yuan Yuan; Lei Chen; | Journal of Intelligent Information Systems | 2023-06-29 |
902 | Unified Language Representation for Question Answering Over Text, Tables, and Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we call for an alternative paradigm, which transforms the images and tables into unified language representations, so that we can simplify the task into a simpler textual QA problem that can be solved using three steps: retrieval, ranking, and generation, all within a language space. |
Bowen Yu; Cheng Fu; Haiyang Yu; Fei Huang; Yongbin Li; | arxiv-cs.CL | 2023-06-29 |
903 | SE-PQA: Personalized Community Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We describe the characteristics of SE-PQA and detail the features associated with questions and answers. |
Pranav Kasela; Marco Braga; Gabriella Pasi; Raffaele Perego; | arxiv-cs.IR | 2023-06-28 |
904 | Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose an automatic data generation pipeline for pre-training passage retrieval models for OK-VQA tasks. |
Alireza Salemi; Mahta Rafiee; Hamed Zamani; | arxiv-cs.IR | 2023-06-28 |
905 | QASA: Advanced Question Answering on Scientific Articles Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on our intensive think-aloud study that revealed the three types of questions: surface, testing, and deep questions, we first propose the QASA benchmark that consists of 1798 novel question answering pairs that require full-stack reasoning on scientific articles in AI and ML fields. Then we propose the QASA approach that tackles the full-stack reasoning with large language models via associative selection, evidential rationale-generation, and systematic composition. |
YOONJOO LEE et. al. | icml | 2023-06-27 |
906 | Motion Question Answering Via Modular Motion Programs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In order to build artificial intelligence systems that can perceive and reason with human behavior in the real world, we must first design models that conduct complex spatio-temporal reasoning over motion sequences. Moving towards this goal, we propose the HumanMotionQA task to evaluate complex, multi-step reasoning abilities of models on long-form human motion sequences |
Mark Endo; Joy Hsu; Jiaman Li; Jiajun Wu; | icml | 2023-06-27 |
907 | Variational Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce the Variational Open-Domain (VOD) framework for end-to-end training and evaluation of retrieval-augmented models, focusing on open-domain question answering and language modelling. |
Valentin Liévin; Andreas Geert Motzfeldt; Ida Riis Jensen; Ole Winther; | icml | 2023-06-27 |
908 | ILLUME: Rationalizing Vision-Language Models Through Human Interactions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, outputs of these models rarely align with user’s rationales for specific answers. In order to improve this alignment and reinforce commonsense reasons, we propose a tuning paradigm based on human interactions with machine-generated data. |
Manuel Brack; Patrick Schramowski; Björn Deiseroth; Kristian Kersting; | icml | 2023-06-27 |
909 | Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel multi-modal framework for Scene Text Visual Question Answering (STVQA), which requires models to read scene text in images for question answering. |
YONGXIN ZHU et. al. | aaai | 2023-06-26 |
910 | Knowledge-Constrained Answer Generation for Open-Ended Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Knowledge-constrained Generative VideoQA Algorithm (KcGA) with an encoder-decoder pipeline, which enables out-of-domain answer generation through an adaptive external knowledge module and a multi-stream information control mechanism. |
YAO JIN et. al. | aaai | 2023-06-26 |
911 | LIQUID: A Framework for List Question Answering Dataset Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although several recent studies have aimed to generate synthetic questions with single-span answers, no study has been conducted on the creation of list questions with multiple, non-contiguous spans as answers. To address this gap, we propose LIQUID, an automated framework for generating list QA datasets from unlabeled corpora. |
Seongyun Lee; Hyunjae Kim; Jaewoo Kang; | aaai | 2023-06-26 |
912 | MPMQA: Multimodal Question Answering on Product Manuals Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, to emphasize the importance of multimodal contents, we propose a Multimodal Product Manual Question Answering (MPMQA) task. |
Liang Zhang; Anwen Hu; Jing Zhang; Shuo Hu; Qin Jin; | aaai | 2023-06-26 |
913 | RINK: Reader-Inherited Evidence Reranker for Table-and-Text Open Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a Retriever-Reranker-Reader framework by newly proposing a Reader-INherited evidence reranKer (RINK) where a reranker module is designed by finetuning the reader’s neural architecture based on a simple prompting method. |
EUNHWAN PARK et. al. | aaai | 2023-06-26 |
914 | FiTs: Fine-Grained Two-Stage Training for Knowledge-Aware Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite the promising result of recent KAQA systems which tend to integrate linguistic knowledge from pre-trained language models (PLM) and factual knowledge from knowledge graphs (KG) to answer complex questions, a bottleneck exists in effectively fusing the representations from PLMs and KGs because of (i) the semantic and distributional gaps between them, and (ii) the difficulties in joint reasoning over the provided knowledge from both modalities. To address the above two problems, we propose a Fine-grained Two-stage training framework (FiTs) to boost the KAQA system performance: The first stage aims at aligning representations from the PLM and the KG, thus bridging the modality gaps between them, named knowledge adaptive post-training. |
Qichen Ye; Bowen Cao; Nuo Chen; Weiyuan Xu; Yuexian Zou; | aaai | 2023-06-26 |
915 | Fauno: The Italian Large Language Model That Will Leave You Senza Parole! Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents Fauno, the first and largest open-source Italian conversational Large Language Model (LLM). |
Andrea Bacciu; Giovanni Trappolini; Andrea Santilli; Emanuele Rodolà; Fabrizio Silvestri; | arxiv-cs.CL | 2023-06-26 |
916 | Improving The Cross-Lingual Generalisation in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While several benefits were realized for multilingual vision-language pretrained models, recent benchmarks across various tasks and languages showed poor cross-lingual generalisation when multilingually pre-trained vision-language models are applied to non-English data, with a large gap between (supervised) English performance and (zero-shot) cross-lingual transfer. In this work, we explore the poor performance of these models on a zero-shot cross-lingual visual question answering (VQA) task, where models are fine-tuned on English visual-question data and evaluated on 7 typologically diverse languages. |
Farhad Nooralahzadeh; Rico Sennrich; | aaai | 2023-06-26 |
917 | STOA-VLP: Spatial-Temporal Modeling of Object and Action for Video-Language Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose STOA-VLP, a pre-training framework that jointly models object and action information across spatial and temporal dimensions. |
WEIHONG ZHONG et. al. | aaai | 2023-06-26 |
918 | COCA: COllaborative CAusal Regularization for Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Through detailed causal-graph analyses and careful inspections of their learning processes, we reveal that AVQA models are not only prone to over-exploit prevalent language bias, but also suffer from additional joint-modal biases caused by the shortcut relations between textual-auditory/visual co-occurrences and dominated answers. In this paper, we propose a COllabrative CAusal (COCA) Regularization to remedy this more challenging issue of data biases. |
MINGRUI LAO et. al. | aaai | 2023-06-26 |
919 | Which Shortcut Solution Do Question Answering Models Prefer to Learn? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Thus, we first examine the learnability of the representative shortcuts on extractive and multiple-choice QA datasets. Behavioral tests using biased training sets reveal that shortcuts that exploit answer positions and word-label correlations are preferentially learned for extractive and multiple-choice QA, respectively. We find that the more learnable a shortcut is, the flatter and deeper the loss landscape is around the shortcut solution in the parameter space. |
Kazutoshi Shinoda; Saku Sugawara; Akiko Aizawa; | aaai | 2023-06-26 |
920 | Symbolic Replay: Scene Graph As Prompt for Continual Learning on VQA Task IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Thus, we propose a real-data-free replay-based method tailored for CL on VQA, named Scene Graph as Prompt for Symbolic Replay. |
STAN WEIXIAN LEI et. al. | aaai | 2023-06-26 |
921 | HybridPrompt: Bridging Language Models and Human Priors in Prompt Tuning for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: They generally do not integrate human priors to compensate for universal knowledge from language models, so as to fit the challenging VQA problem and generate reliable answers. To address these issues, we propose HybridPrompt, a cloze- and verify-style hybrid prompt framework with bridging language models and human priors in prompt tuning for VQA. |
Zhiyuan Ma; Zhihuan Yu; Jianjun Li; Guohui Li; | aaai | 2023-06-26 |
922 | Question Decomposition Tree for Answering Complex Questions Over Knowledge Bases Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Question Decomposition Tree (QDT) to represent the structure of complex questions. |
Xiang Huang; Sitao Cheng; Yiheng Shu; Yuheng Bao; Yuzhong Qu; | aaai | 2023-06-26 |
923 | A Question-Answering Approach to Key Value Pair Extraction from Form-Like Document Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a new question-answering (QA) based key-value pair extraction approach, called KVPFormer, to robustly extracting key-value relationships between entities from form-like document images. |
KAI HU et. al. | aaai | 2023-06-26 |
924 | FunQA: Towards Surprising Video Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce FunQA, a challenging video question-answering (QA) dataset specifically designed to evaluate and enhance the depth of video reasoning based on counter-intuitive and fun videos. |
BINZHU XIE et. al. | arxiv-cs.CV | 2023-06-26 |
925 | Inferential Knowledge-Enhanced Integrated Reasoning for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose an Inferential Knowledge-Enhanced Integrated Reasoning method. |
Jianguo Mao; Wenbin Jiang; Hong Liu; Xiangdong Wang; Yajuan Lyu; | aaai | 2023-06-26 |
926 | RPA: Reasoning Path Augmentation in Iterative Retrieving for Multi-Hop QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Within the RP, two fundamental challenges emerge for better performance: (i) what the order of the justifications in the RP should be, and (ii) what if the wrong justification has been in the path. In this paper, we propose Reasoning Path Augmentation (RPA), which uses reasoning path reordering and augmentation to handle the above two challenges, respectively. |
Ziyi Cao; Bingquan Liu; Shaobo Li; | aaai | 2023-06-26 |
927 | Efficient End-to-End Video Question Answering with Pyramidal Multimodal Transformer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a new method for end-to-end Video Question Answering (VideoQA), aside from the current popularity of using large-scale pre-training with huge feature extractors. |
Min Peng; Chongyang Wang; Yu Shi; Xiang-Dong Zhou; | aaai | 2023-06-26 |
928 | Relation-Aware Language-Graph Transformer for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, most existing GNN-based modules for QA do not take advantage of rich relational information of KGs and depend on limited information interaction between the LM and the KG. To address these issues, we propose Question Answering Transformer (QAT), which is designed to jointly reason over language and graphs with respect to entity relations in a unified manner. |
JINYOUNG PARK et. al. | aaai | 2023-06-26 |
929 | SPRING: Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graph Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing multimodal conversation agents have shown impressive abilities to locate absolute positions or retrieve attributes in simple scenarios, but they fail to perform well when complex relative positions and information alignments are involved, which poses a bottleneck in response quality. In this paper, we propose a Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graph (SPRING) with abilities of reasoning multi-hops spatial relations and connecting them with visual attributes in crowded situated scenarios. |
YUXING LONG et. al. | aaai | 2023-06-26 |
930 | SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we propose a new multi-image document VQA dataset, SlideVQA, containing 2.6k+ slide decks composed of 52k+ slide images and 14.5k questions about a slide deck. |
RYOTA TANAKA et. al. | aaai | 2023-06-26 |
931 | Visual Question Answering in Remote Sensing with Cross-Attention and Multimodal Information Bottleneck Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this research, we deal with the problem of visual question answering (VQA) in remote sensing. |
Jayesh Songara; Shivam Pande; Shabnam Choudhury; Biplab Banerjee; Rajbabu Velmurugan; | arxiv-cs.CV | 2023-06-25 |
932 | Retrieving Supporting Evidence for LLMs Generated Answers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we report a simple experiment to automatically verify generated answers against a corpus. |
Siqing Huo; Negar Arabzadeh; Charles L. A. Clarke; | arxiv-cs.IR | 2023-06-23 |
933 | CompMix: A Benchmark for Heterogeneous Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Fact-centric question answering (QA) often requires access to multiple, heterogeneous, information sources. By jointly considering several sources like a knowledge base (KB), a … |
Philipp Christmann; Rishiraj Saha Roy; Gerhard Weikum; | arxiv-cs.IR | 2023-06-21 |
934 | EQUALS: A Real-world Dataset for Legal Question Answering Via Reading Chinese Laws Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Legal Question Answering (LQA) is a promising artificial intelligence application with high practical value. A professional and effective legal question answering (QA) agent can … |
ANDONG CHEN et. al. | Proceedings of the Nineteenth International Conference on … | 2023-06-19 |
935 | Temporal-Weighted Bipartite Graph Model for Sparse Expert Recommendation in Community Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Community Question Answering (CQA) websites are valuable knowledge repositories where individuals exchange information by asking and answering questions. With an ever-increasing … |
Vaibhav Krishna; Nino Antulov-Fantulin; | Proceedings of the 31st ACM Conference on User Modeling, … | 2023-06-18 |
936 | Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we explore effective prompting techniques to enhance zero- and few-shot Visual Question Answering (VQA) performance in contemporary Vision-Language Models (VLMs). |
Rabiul Awal; Le Zhang; Aishwarya Agrawal; | arxiv-cs.CV | 2023-06-16 |
937 | Learning to Summarize and Answer Questions About A Virtual Robot’s Past Actions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To enable training of question answering, we develop a method to automatically generate English-language questions and answers about objects, actions, and the temporal order in which actions occurred during episodes of robot action in the virtual environment. |
Chad DeChant; Iretiayo Akinola; Daniel Bauer; | arxiv-cs.RO | 2023-06-16 |
938 | LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a comprehensive evaluation of publicly available large multimodal models by building a LVLM evaluation Hub (LVLM-eHub). |
PENG XU et. al. | arxiv-cs.CV | 2023-06-15 |
939 | Encyclopedic VQA: Visual Questions About Detailed Properties of Fine-grained Categories Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose Encyclopedic-VQA, a large scale visual question answering (VQA) dataset featuring visual questions about detailed properties of fine-grained categories and instances. |
THOMAS MENSINK et. al. | arxiv-cs.CV | 2023-06-15 |
940 | Retrieving-to-Answer: Zero-Shot Video Question Answering with Frozen Large Language Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Video Question Answering (VideoQA) has been significantly advanced from the scaling of recent Large Language Models (LLMs). The key idea is to convert the visual information into … |
JUNTING PAN et. al. | ArXiv | 2023-06-15 |
941 | Improving Selective Visual Question Answering By Learning from Your Peers IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we explore Selective VQA in both in-distribution (ID) and OOD scenarios, where models are presented with mixtures of ID and OOD data. |
CORENTIN DANCETTE et. al. | arxiv-cs.CV | 2023-06-14 |
942 | Maestro: Automatic Generation of Comprehensive Benchmarks for Question Answering Over Knowledge Graphs Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Recently, there has been an upsurge in the number of knowledge graphs (KG) that can only be accessed by experts. Non-expert users lack an adequate understanding of the queried … |
Abdelghny Orogat; Ahmed El-Roby; | Proceedings of the ACM on Management of Data | 2023-06-13 |
943 | AVIS: Autonomous Visual Information Seeking with Large Language Model Agent Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an autonomous information seeking visual question answering framework, AVIS. |
ZINIU HU et. al. | arxiv-cs.CV | 2023-06-13 |
944 | Explicit Knowledge Integration for Knowledge-Aware Visual Question Answering About Named Entities Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Recent years have shown unprecedented growth of interest in Vision-Language related tasks, with the need to address the inherent challenges of integrating linguistic and visual … |
Omar Adjali; Paul Grimal; Olivier Ferret; Sahar Ghannay; Hervé Le Borgne; | Proceedings of the 2023 ACM International Conference on … | 2023-06-12 |
945 | A Video Question Answering Model Based on Knowledge Distillation Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Video question answering (QA) is a cross-modal task that requires understanding the video content to answer questions. Current techniques address this challenge by employing … |
Zhuang Shao; Jiahui Wan; Linlin Zong; | Inf. | 2023-06-12 |
946 | Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose LEDO: a model-agnostic and computationally efficient framework for Label Error Detection and Overwrite. |
XIAO YANG et. al. | arxiv-cs.CL | 2023-06-12 |
947 | MyEachtra: Event-Based Interactive Lifelog Retrieval System for LSC’23 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Retrieval is a fundamental challenge within the research community of lifelog and the Lifelog Search Challenge (LSC) has been an important annual benchmarking activity for … |
Ly-Duyen Tran; Binh T. Nguyen; Liting Zhou; C. Gurrin; | Proceedings of the 6th Annual ACM Lifelog Search Challenge | 2023-06-12 |
948 | Knowledge Detection By Relevant Question and Image Attributes in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Some testing questions require external knowledge to derive a solution. |
Param Ahir; Dr. Hiteishi Diwanji; | arxiv-cs.CV | 2023-06-08 |
949 | Privacy Aware Question-Answering System for Online Mental Health Risk Assessment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a Question-Answering (QA) approach to assess mental health risk using the Unified-QA model on two large mental health datasets. |
Prateek Chhikara; Ujjwal Pasupulety; John Marshall; Dhiraj Chaurasia; Shweta Kumari; | arxiv-cs.CL | 2023-06-08 |
950 | Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose to augment the knowledge directly in the input of LLMs. |
Jinheon Baek; Alham Fikri Aji; Amir Saffari; | arxiv-cs.CL | 2023-06-07 |
951 | When to Read Documents or QA History: On Unified and Selective Open-domain QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper studies the problem of open-domain question answering, with the aim of answering a diverse range of questions leveraging knowledge resources. |
Kyungjae Lee; Sang-eun Han; Seung-won Hwang; Moontae Lee; | arxiv-cs.CL | 2023-06-07 |
952 | Enhancing In-Context Learning with Answer Feedback for Multi-Span Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: A popular implementation is to concatenate a few questions and their correct answers through simple templates, informing LLM of the desired output. In this paper, we propose a novel way of employing labeled data such that it also informs LLM of some undesired output, by extending demonstration examples with feedback about answers predicted by an off-the-shelf model, e.g., correct, incorrect, or incomplete. |
Zixian Huang; Jiaying Zhou; Gengyang Xiao; Gong Cheng; | arxiv-cs.CL | 2023-06-07 |
953 | Improving Vietnamese Legal Question–Answering System Based on Automatic Data Enrichment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It is even more difficult to perform legal QA for low-resource languages like Vietnamese where labeled data are rare and pre-trained language models are still limited. In this paper, we try to overcome these limitations by implementing a Vietnamese article-level retrieval-based legal QA system and introduce a novel method to improve the performance of language models by improving data quality through weak labeling. |
Thi-Hai-Yen Vuong; Ha-Thanh Nguyen; Quang-Huy Nguyen; Le-Minh Nguyen; Xuan-Hieu Phan; | arxiv-cs.CL | 2023-06-07 |
954 | Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling Via Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a method to directly predict answers with a phrase retrieval scheme for a sequence of words, reducing the conventional two distinct subtasks into a single one. |
Soyeong Jeong; Jinheon Baek; Sung Ju Hwang; Jong C. Park; | arxiv-cs.CL | 2023-06-07 |
955 | Gotta: Generative Few-shot Question Answering By Prompt-based Cloze Data Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we develop Gotta, a Generative prOmpT-based daTa Augmentation framework to mitigate the challenge above. |
Xiusi Chen; Yu Zhang; Jinliang Deng; Jyun-Yu Jiang; Wei Wang; | arxiv-cs.CL | 2023-06-06 |
956 | Triggering Multi-Hop Reasoning for Question Answering in Language Models Using Soft Prompts and Random Walks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite readily memorizing world knowledge about entities, pre-trained language models (LMs) struggle to compose together two or more facts to perform multi-hop reasoning in question-answering tasks. In this work, we propose techniques that improve upon this limitation by relying on random walks over structured knowledge graphs. |
Kanishka Misra; Cicero Nogueira dos Santos; Siamak Shakeri; | arxiv-cs.CL | 2023-06-06 |
957 | Diversifying Joint Vision-Language Tokenization Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we find that the representations must not only jointly capture features from both modalities but should also be diverse for better generalization performance. |
Vardaan Pahuja; AJ Piergiovanni; Anelia Angelova; | arxiv-cs.CV | 2023-06-06 |
958 | Benchmarking Large Language Models on CMExam — A Comprehensive Chinese Medical Exam Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam, sourced from the Chinese National Medical Licensing Examination. |
JUNLING LIU et. al. | arxiv-cs.CL | 2023-06-05 |
959 | OECA-Net: A Co-attention Network for Visual Question Answering Based on OCR Scene Text Feature Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View |
F. Yan; Wushouer Silamu; Yachuang Chai; Yanbing Li; | Multim. Tools Appl. | 2023-06-05 |
960 | Evaluation of AI Chatbots for Patient-Specific EHR Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper investigates the use of artificial intelligence chatbots for patient-specific question answering (QA) from clinical notes using several large language model (LLM) based systems: ChatGPT (versions 3.5 and 4), Google Bard, and Claude. |
Alaleh Hamidi; Kirk Roberts; | arxiv-cs.CL | 2023-06-04 |
961 | Multi-CLIP: Contrastive Vision-Language Pre-training for Question Answering Tasks in 3D Scenes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel 3D pre-training Vision-Language method, namely Multi-CLIP, that enables a model to learn language-grounded and transferable 3D scene point cloud representations. |
ALEXANDROS DELITZAS et. al. | arxiv-cs.CV | 2023-06-04 |
962 | Question-Context Alignment and Answer-Context Dependencies for Effective Answer Sentence Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to improve the candidate scoring by explicitly incorporating the dependencies between question-context and answer-context into the final representation of a candidate. |
MINH VAN NGUYEN et. al. | arxiv-cs.CL | 2023-06-03 |
963 | Reimagining Retrieval Augmented Language Models for Answering Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison. |
WANG-CHIEW TAN et. al. | arxiv-cs.CL | 2023-06-01 |
964 | Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we first investigate what is a key factor for the success of existing PEFT methods, and realize that it’s essential to preserve the PLM’s starting point when initializing a PEFT method. With this finding, we propose memory-efficient fine-tuning (MEFT) that inserts adapters into a PLM, preserving the PLM’s starting point and making it reversible without additional pre-training. |
Baohao Liao; Shaomu Tan; Christof Monz; | arxiv-cs.CL | 2023-06-01 |
965 | Overcoming Language Bias in Remote Sensing Visual Question Answering Via Adversarial Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, VQA models commonly face the challenge of language bias, resulting from the learned superficial correlation between questions and answers. To address this issue, in this study, we present a novel framework to reduce the language bias of the VQA for remote sensing data (RSVQA). |
Zhenghang Yuan; Lichao Mou; Xiao Xiang Zhu; | arxiv-cs.CV | 2023-06-01 |
966 | LiT-4-RSVQA: Lightweight Transformer-based Visual Question Answering in Remote Sensing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Visual question answering (VQA) methods in remote sensing (RS) aim to answer natural language questions with respect to an RS image. |
Leonard Hackel; Kai Norman Clasen; Mahdyar Ravanbakhsh; Begüm Demir; | arxiv-cs.CV | 2023-06-01 |
967 | Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Instead, in this paper, we find that instruction-tuning language models like Claude and ChatGPT can understand layout by spaces and line breaks. |
Wenjin Wang; Yunhao Li; Yixin Ou; Yin Zhang; | arxiv-cs.CL | 2023-06-01 |
968 | TimelineQA: A Benchmark for Question Answering Over Timelines Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We describe a set of experiments on TimelineQA with several state-of-the-art QA models. |
WANG-CHIEW TAN et. al. | arxiv-cs.CL | 2023-06-01 |
969 | Building Extractive Question Answering System to Support Human-AI Health Coaching Model for Sleep Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a human-Artificial Intelligence (AI) health coaching model incorporating a domain-specific extractive QA system. |
Iva Bojic; Qi Chwen Ong; Shafiq Joty; Josip Car; | arxiv-cs.CL | 2023-05-31 |
970 | Using Visual Cropping to Enhance Fine-Detail Question Answering of BLIP-Family Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Given the recent success of the BLIP-family models, we study a zero-shot and a fine-tuned BLIP model. |
Jiarui Zhang; Mahyar Khayatkhoei; Prateek Chhikara; Filip Ilievski; | arxiv-cs.CV | 2023-05-31 |
971 | Attention-Based Methods For Audio Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose neural network architectures based on self-attention and cross-attention for the AQA task. |
Parthasaarathy Sudarsanam; Tuomas Virtanen; | arxiv-cs.CL | 2023-05-31 |
972 | UKP-SQuARE: An Interactive Tool for Teaching Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce UKP-SQuARE as a platform for QA education. |
Haishuo Fang; Haritz Puerto; Iryna Gurevych; | arxiv-cs.CL | 2023-05-31 |
973 | Graph Reasoning for Question Answering with Triplet Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple yet effective method to first retrieve the most relevant triplets from KGs and then rerank them, which are then concatenated with questions to be fed into language models. |
SHIYANG LI et. al. | arxiv-cs.CL | 2023-05-30 |
974 | Generate Then Select: Open-ended Visual Question Answering Guided By World Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the aforementioned challenges, we propose RASO: a new VQA pipeline that deploys a generate-then-select strategy guided by world knowledge for the first time. |
XINGYU FU et. al. | arxiv-cs.CL | 2023-05-30 |
975 | KEYword Based Sampling (KEYS) for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the research community, very little focus is on how humans generate answers to a question and how this behavior can be incorporated in a language model. In this paper, we want to explore these two areas combined, i.e., how sampling can be to used generate answers which are close to human-like behavior and factually correct. |
Jyothir S V; Zuhaib Akhtar; | arxiv-cs.CL | 2023-05-29 |
976 | PaLI-X: On Scaling Up A Multilingual Vision and Language Model IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture. |
XI CHEN et. al. | arxiv-cs.CV | 2023-05-29 |
977 | Multi-Scale Attention for Audio Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we present a Multi-scale Window Attention Fusion Model (MWAFM) consisting of an asynchronous hybrid attention module and a multi-scale window attention module. |
Guangyao Li; Yixin Xu; Di Hu; | arxiv-cs.SD | 2023-05-29 |
978 | Contextual Object Detection with Multimodal Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recent Multimodal Large Language Models (MLLMs) are remarkable in vision-language tasks, such as image captioning and question answering, but lack the essential perception ability, i.e., object detection. In this work, we address this limitation by introducing a novel research problem of contextual object detection — understanding visible objects within different human-AI interactive contexts. |
Yuhang Zang; Wei Li; Jun Han; Kaiyang Zhou; Chen Change Loy; | arxiv-cs.CV | 2023-05-29 |
979 | Breaking Language Barriers with A LEAP: Learning Strategies for Polyglot LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Through systematic investigation and evaluation of diverse languages using popular question-answering (QA) datasets, we present novel techniques that unlock the true potential of LLMs in a polyglot landscape. |
AKSHAY NAMBI et. al. | arxiv-cs.CL | 2023-05-28 |
980 | HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents HaVQA, the first multimodal dataset for visual question-answering (VQA) tasks in the Hausa language. |
SHANTIPRIYA PARIDA et. al. | arxiv-cs.CL | 2023-05-28 |
981 | Conformal Prediction with Large Language Models for Multi-Choice Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we explore how conformal prediction can be used to provide uncertainty quantification in language models for the specific task of multiple-choice question-answering. |
BHAWESH KUMAR et. al. | arxiv-cs.CL | 2023-05-28 |
982 | Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose EAR, a query Expansion And Reranking approach for improving passage retrieval, with the application to open-domain question answering. |
Yung-Sung Chuang; Wei Fang; Shang-Wen Li; Wen-tau Yih; James Glass; | arxiv-cs.CL | 2023-05-26 |
983 | Exploiting Abstract Meaning Representation for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a method known as Graph-as-Token (GST) to incorporate AMRs into PLMs. |
CUNXIANG WANG et. al. | arxiv-cs.CL | 2023-05-26 |
984 | RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although representative models like Fusion-in-Decoder (FiD) have been proposed to address this challenge, these systems can inadvertently rely on spurious features instead of genuine causal relationships between the question and the passages to generate answers. To counter this problem, we introduce the Rational Fusion-in-Decoder (RFiD) model. |
Cunxiang Wang; Haofei Yu; Yue Zhang; | arxiv-cs.CL | 2023-05-26 |
985 | Zero-shot Visual Question Answering with Language Model Feedback Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel language model guided captioning approach, LAMOC, for knowledge-based visual question answering (VQA). |
Yifan Du; Junyi Li; Tianyi Tang; Wayne Xin Zhao; Ji-Rong Wen; | arxiv-cs.CV | 2023-05-26 |
986 | An Empirical Comparison of LM-based Question and Answer Generation Methods Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we establish baselines with three different QAG methodologies that leverage sequence-to-sequence language model (LM) fine-tuning. |
Asahi Ushio; Fernando Alva-Manchego; Jose Camacho-Collados; | arxiv-cs.CL | 2023-05-26 |
987 | UFO: Unified Fact Obtaining for Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a Unified Facts Obtaining (UFO) approach. |
Zhifeng Li; Yifan Fan; Bowei Zou; Yu Hong; | arxiv-cs.CL | 2023-05-25 |
988 | BUCA: A Binary Classification Approach to Unsupervised Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to transform the downstream multiple choice question answering task into a simpler binary classification task by ranking all candidate answers according to their reasonableness. |
Jie He; Simon Chi Lok U; Víctor Gutiérrez-Basulto; Jeff Z. Pan; | arxiv-cs.CL | 2023-05-25 |
989 | The Dangers of Trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that task-based systems which exhibit certain advanced linguistic dialog behaviors, such as lexical alignment (repeating what the user said), are in fact preferred and trusted more, whereas other phenomena, such as pronouns and ellipsis are dis-preferred. |
SABRINA CHIESURIN et. al. | arxiv-cs.CL | 2023-05-25 |
990 | NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a novel visual question answering (VQA) task in the context of autonomous driving, aiming to answer natural language questions based on street-view clues. |
Tianwen Qian; Jingjing Chen; Linhai Zhuo; Yang Jiao; Yu-Gang Jiang; | arxiv-cs.CV | 2023-05-24 |
991 | Mitigating Temporal Misalignment By Discarding Outdated Facts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To mitigate the effects of temporal misalignment, we propose fact duration prediction: the task of predicting how long a given fact will remain true. |
Michael J. Q. Zhang; Eunsol Choi; | arxiv-cs.CL | 2023-05-24 |
992 | Comparing Humans and Models on A Similar Scale: Towards Cognitive Gender Bias Evaluation in Coreference Resolution Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work we address the question: can we quantify the extent to which model biases reflect human behaviour? |
Gili Lior; Gabriel Stanovsky; | arxiv-cs.CL | 2023-05-24 |
993 | Peek Across: Improving Multi-Document Modeling Via Cross-Document Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The integration of multi-document pre-training objectives into language models has resulted in remarkable improvements in multi-document downstream tasks. In this work, we propose extending this idea by pre-training a generic multi-document model from a novel cross-document question answering pre-training objective. |
Avi Caciularu; Matthew E. Peters; Jacob Goldberger; Ido Dagan; Arman Cohan; | arxiv-cs.CL | 2023-05-24 |
994 | Reasoning Over Hierarchical Question Decomposition Tree for Explainable Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to leverage question decomposing for heterogeneous knowledge integration, by breaking down a complex question into simpler ones, and selecting the appropriate knowledge source for each sub-question. |
JIAJIE ZHANG et. al. | arxiv-cs.CL | 2023-05-24 |
995 | Dynamic Clue Bottlenecks: Towards Interpretable-by-Design Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While post-hoc rationales offer certain insight into understanding model behavior, these explanations are not guaranteed to be faithful to the model. In this paper, we address these shortcomings by introducing an interpretable by design model that factors model decisions into intermediate human-legible explanations, and allows people to easily understand why a model fails or succeeds. |
Xingyu Fu; Ben Zhou; Sihao Chen; Mark Yatskar; Dan Roth; | arxiv-cs.CL | 2023-05-24 |
996 | Extracting Psychological Indicators Using Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a method for extracting text spans that may indicate one of the BIG5 psychological traits using a question-answering task with examples that have no answer for the asked question. |
Luka Pavlović; | arxiv-cs.CL | 2023-05-24 |
997 | CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, two bottlenecks limit these approaches: the inherent incompleteness of CSKBs limits the semantic coverage of synthetic QA pairs, and the lack of human annotations makes the sampled negative examples potentially uninformative and contradictory. To tackle these limitations above, we propose Conceptualization-Augmented Reasoner (CAR), a zero-shot commonsense question-answering framework that fully leverages the power of conceptualization. |
WEIQI WANG et. al. | arxiv-cs.CL | 2023-05-24 |
998 | Is Summary Useful or Not? An Extrinsic Human Evaluation of Text Summaries on Downstream Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We carefully design three different downstream tasks for extrinsic human evaluation of summaries, i.e., question answering, text classification and text similarity assessment. |
Xiao Pu; Mingqi Gao; Xiaojun Wan; | arxiv-cs.CL | 2023-05-24 |
999 | Unlocking Temporal Question Answering for Large Language Models Using Code Execution Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our preliminary experiments show that generating intermediate reasoning steps does not always boost the performance of complex temporal question-answering tasks. Therefore, we propose a novel framework that combines the extraction capability of LLMs and the logical reasoning capability of a Python solver to tackle this issue. |
XINGXUAN LI et. al. | arxiv-cs.CL | 2023-05-24 |
1000 | Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Chain-of-Questions, a framework that trains a model to generate sub-questions and sub-answers one at a time by leveraging human annotated question decomposition meaning representation (QDMR). |
Wang Zhu; Jesse Thomason; Robin Jia; | arxiv-cs.CL | 2023-05-24 |
1001 | Asking Clarification Questions to Handle Ambiguity in Open-Domain QA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Instead, we propose to ask a clarification question, where the user’s response will help identify the interpretation that best aligns with the user’s intention. |
DONGRYEOL LEE et. al. | arxiv-cs.CL | 2023-05-23 |
1002 | Few-shot Unified Question Answering: Tuning Models or Prompts? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The paper provides an exhaustive analysis of their applicability using 16 QA datasets, revealing that prompt tuning can perform as well as model tuning in a few-shot setting with a good initialization. |
Srijan Bansal; Semih Yavuz; Bo Pang; Meghana Bhat; Yingbo Zhou; | arxiv-cs.CL | 2023-05-23 |
1003 | Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we collect minimally edited questions as challenging contrast sets to evaluate OpenQA models. |
Zhihan Zhang; Wenhao Yu; Zheng Ning; Mingxuan Ju; Meng Jiang; | arxiv-cs.CL | 2023-05-23 |
1004 | Few-Shot Data Synthesis for Open Domain Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To improve performance of smaller language models, we propose a data synthesis framework for multi-hop question answering that requires less than 10 human annotated question answer pairs. |
Mingda Chen; Xilun Chen; Wen-tau Yih; | arxiv-cs.CL | 2023-05-23 |
1005 | TACR: A Table-alignment-based Cell-selection and Reasoning Model for Hybrid Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such a challenge made it difficult for previous studies to show their reasoning ability in retrieving answers. To bridge this gap, we propose a novel Table-alignment-based Cell-selection and Reasoning model (TACR) for hybrid text and table QA, evaluated on the HybridQA and WikiTableQuestions datasets. |
JIAN WU et. al. | arxiv-cs.CL | 2023-05-23 |
1006 | Evaluating and Modeling Attribution for Cross-Lingual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We find that Natural Language Inference models and PaLM 2 fine-tuned on a very small amount of attribution data can accurately detect attribution. Based on these models, we improve the attribution level of a cross-lingual question-answering system. |
BENJAMIN MULLER et. al. | arxiv-cs.CL | 2023-05-23 |
1007 | RET-LLM: Towards A General Read-Write Memory for Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing LLMs lack a dedicated memory unit, limiting their ability to explicitly store and retrieve knowledge for various tasks. In this paper, we propose RET-LLM a novel framework that equips LLMs with a general write-read memory unit, allowing them to extract, store, and recall knowledge from the text as needed for task performance. |
Ali Modarressi; Ayyoob Imani; Mohsen Fayyaz; Hinrich Schütze; | arxiv-cs.CL | 2023-05-23 |
1008 | Getting MoRE Out of Mixture of Language Model Reasoning Experts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We provide empirical evidence that state-of-the-art LLMs suffer from poor generalizability on reasoning types beyond those seen in the prompt. To remedy this, we propose a Mixture-of-Reasoning-Experts (MoRE) framework that ensembles diverse specialized language models. |
Chenglei Si; Weijia Shi; Chen Zhao; Luke Zettlemoyer; Jordan Boyd-Graber; | arxiv-cs.CL | 2023-05-23 |
1009 | Make A Choice! Knowledge Base Question Answering with In-Context Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present McL-KBQA, a framework that incorporates the few-shot ability of LLM into the KBQA method via ICL-based multiple choice and then improves the effectiveness of the QA tasks. |
Chuanyuan Tan; Yuehe Chen; Wenbiao Shao; Wenliang Chen; | arxiv-cs.CL | 2023-05-23 |
1010 | InteractiveIE: Towards Assessing The Strength of Human-AI Collaboration in Improving The Performance of Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Since the purpose of question answering intersect with the goal of information extraction, we use automatic question generation to induce template slots from the documents and investigate how a tiny amount of a proxy human-supervision on-the-fly (termed as InteractiveIE) can further boost the performance. |
ISHANI MONDAL et. al. | arxiv-cs.CL | 2023-05-23 |
1011 | Selectively Answering Ambiguous Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We investigate question answering from this perspective, focusing on answering a subset of questions with a high degree of accuracy, from a set of questions in which many are inherently ambiguous. |
JEREMY R. COLE et. al. | arxiv-cs.CL | 2023-05-23 |
1012 | HOP, UNION, GENERATE: Explainable Multi-hop Reasoning Without Rationale Supervision Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work proposes a principled, probabilistic approach for training explainable multi-hop QA systems without rationale supervision. |
Wenting Zhao; Justin T. Chiu; Claire Cardie; Alexander M. Rush; | arxiv-cs.CL | 2023-05-23 |
1013 | DUBLIN — Document Understanding By Language-Image Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose DUBLIN, which is pretrained on web pages using three novel objectives: Masked Document Text Generation Task, Bounding Box Task, and Rendered Question Answering Task, that leverage both the spatial and semantic information in the document images. |
KRITI AGGARWAL et. al. | arxiv-cs.CV | 2023-05-23 |
1014 | Towards Graph-hop Retrieval and Reasoning in Complex Question Answering Over Textual Database Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to conduct Graph-Hop — a novel multi-chains and multi-hops retrieval and reasoning paradigm in complex question answering. |
Minjun Zhu; Yixuan Weng; Shizhu He; Kang Liu; Jun Zhao; | arxiv-cs.CL | 2023-05-23 |
1015 | Continual Dialogue State Tracking Via Example-Guided Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Motivated by the insight that dialogue state tracking (DST), a crucial component of dialogue systems that estimates the user’s goal as a conversation proceeds, is a simple natural language understanding task, we propose reformulating it as a bundle of granular example-guided question answering tasks to minimize the task shift between services and thus benefit continual learning. |
HYUNDONG CHO et. al. | arxiv-cs.CL | 2023-05-23 |
1016 | Knowledge Graphs Querying Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We aim at uniting different interdisciplinary topics and concepts that have been developed for KG querying. |
Arijit Khan; | arxiv-cs.DB | 2023-05-23 |
1017 | BAND: Biomedical Alert News Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing systems lack thorough epidemiological analysis in relation to corresponding alerts or news, largely due to the scarcity of well-annotated reports data. To address this gap, we introduce the Biomedical Alert News Dataset (BAND), which includes 1,508 samples from existing reported news articles, open emails, and alerts, as well as 30 epidemiology-related questions. |
ZIHAO FU et. al. | arxiv-cs.CL | 2023-05-23 |
1018 | AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from The Web Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper we introduce AVeriTeC, a new dataset of 4,568 real-world claims covering fact-checks by 50 different organizations. |
Michael Schlichtkrull; Zhijiang Guo; Andreas Vlachos; | arxiv-cs.CL | 2023-05-22 |
1019 | FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability Through 5W Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite progress in automatic text-based fact verification (e.g., FEVER, LIAR), the research community lacks substantial effort in multimodal fact verification. To address this gap, we introduce FACTIFY 3M, a dataset of 3 million samples that pushes the boundaries of the domain of fact verification via a multimodal fake news dataset, in addition to offering explainability through the concept of 5W question-answering. |
MEGHA CHAKRABORTY et. al. | arxiv-cs.CL | 2023-05-22 |
1020 | VLAB: Enhancing Video Language Pre-training By Feature Adapting and Blending IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there is limited research on learning video-text representations for general video multimodal tasks based on these powerful features. Towards this goal, we propose a novel video-text pre-training method dubbed VLAB: Video Language pre-training by feature Adapting and Blending, which transfers CLIP representations to video pre-training tasks and develops unified video multimodal models for a wide range of video-text tasks. |
XINGJIAN HE et. al. | arxiv-cs.CV | 2023-05-22 |
1021 | Evaluating Open-QA Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a new task, Evaluating QA Evaluation (QA-Eval) and the corresponding dataset EVOUNA, designed to assess the accuracy of AI-generated answers in relation to standard answers within Open-QA. |
CUNXIANG WANG et. al. | arxiv-cs.CL | 2023-05-21 |
1022 | Model Analysis & Evaluation for Ambiguous Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To what extent do these models ground their answers in evidence? In this study, we aim to thoroughly investigate these aspects, and provide valuable insights into the limitations of the current approaches. |
Konstantinos Papakostas; Irene Papadopoulou; | arxiv-cs.CL | 2023-05-21 |
1023 | Target-Aware Spatio-Temporal Reasoning Via Answering Questions in Dynamics Audio-Visual Scenarios Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a new target-aware joint spatio-temporal grounding network for AVQA. |
Yuanyuan Jiang; Jianqin Yin; | arxiv-cs.CV | 2023-05-21 |
1024 | Evaluating Open Question Answering Evaluation Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This study focuses on the evaluation of Open Question Answering (Open-QA) tasks, which have become vital in the realm of artificial intelligence. Current automatic evaluation … |
CUNXIANG WANG et. al. | ArXiv | 2023-05-21 |
1025 | VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The VNHSGE (VietNamese High School Graduation Examination) dataset, developed exclusively for evaluating large language models (LLMs), is introduced in this article. |
DAO XUAN-QUY et. al. | arxiv-cs.CL | 2023-05-20 |
1026 | Self-QA: Unsupervised Knowledge Guided Language Model Alignment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This endeavor necessitates substantial human effort for data annotation and wrestles with issues concerning data quality, diversity, accuracy, and other related factors. To overcome these obstacles, we introduce an innovative framework named Self-QA, which replaces the traditional practice of human-written instruction seeds with a vast amount of unsupervised knowledge, enabling the model to generate a larger quantity of correct and domain-specific instruction data. |
Xuanyu Zhang; Qing Yang; | arxiv-cs.CL | 2023-05-19 |
1027 | Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we provide a benchmark Question Answering (QA) dataset named MSQA, centered around Microsoft products and IT technical problems encountered by customers. |
FANGKAI YANG et. al. | arxiv-cs.CL | 2023-05-19 |
1028 | Surgical-VQLA: Transformer with Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Visual Question Localized-Answering in Robotic Surgery (Surgical-VQLA) to localize the specific surgical area during the answer prediction. |
Long Bai; Mobarakol Islam; Lalithkumar Seenivasan; Hongliang Ren; | arxiv-cs.CV | 2023-05-19 |
1029 | Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel method called Joint QA and DC GEneration (JADE), which utilizes a pre-trained multimodal model and easily-crawled image-text pairs to automatically generate and filter large-scale VQA and dense captioning datasets. |
ZIKANG LIU et. al. | arxiv-cs.CV | 2023-05-19 |
1030 | Surgical-VQLA:Transformer with Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Despite the availability of computer-aided simulators and recorded videos of surgical procedures, junior residents still heavily rely on experts to answer their queries. However, … |
Long Bai; Mobarakol Islam; L. Seenivasan; Hongliang Ren; | 2023 IEEE International Conference on Robotics and … | 2023-05-19 |
1031 | Evaluation of Medium-large Language Models at Zero-shot Closed Book Generative Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The paper introduces an own test dataset and presents results from human evaluation. |
René Peinl; Johannes Wirth; | arxiv-cs.CL | 2023-05-19 |
1032 | Visual Question Answering: A Survey on Techniques and Common Trends in Recent Literature Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, 6 datasets were analyzed and provided their link to download. In this work, several recent pieces of research in this area were investigated and a deeper analysis and comparison among them were provided, including results, the state-of-the-art, common errors, and possible points of improvement for future researchers. |
ANA CLÁUDIA AKEMI MATSUKI DE FARIA et. al. | arxiv-cs.CV | 2023-05-18 |
1033 | MLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present our work on developing a multilingual, efficient text-to-text transformer that is suitable for handling long inputs. |
David Uthus; Santiago Ontañón; Joshua Ainslie; Mandy Guo; | arxiv-cs.CL | 2023-05-18 |
1034 | Writing Your Own Book: A Method for Going from Closed to Open Book QA to Improve Robustness and Performance of Smaller LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce two novel methods, Tree-Search and Self-contextualizing QA, designed to enhance the performance of large language models (LLMs) in question-answering tasks. |
Giorgi Kokaia; Pratyush Sinha; Yutong Jiang; Nozha Boujemaa; | arxiv-cs.CL | 2023-05-18 |
1035 | Aligning Instruction Tasks Unlocks Large Language Models As Zero-Shot Relation Extractors IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We hypothesize that instruction-tuning has been unable to elicit strong RE capabilities in LLMs due to RE’s low incidence in instruction-tuning datasets, making up less than 1% of all tasks (Wang et al., 2022). To address this limitation, we propose QA4RE, a framework that aligns RE with question answering (QA), a predominant task in instruction-tuning datasets. |
Kai Zhang; Bernal Jiménez Gutiérrez; Yu Su; | arxiv-cs.CL | 2023-05-18 |
1036 | Prompting Large Language Models With Answer Heuristics for Knowledge-Based Visual Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present Prophet—a conceptually simple framework designed to prompt GPT-3 with answer heuristics for knowledge-based VQA. |
Zhenwei Shao; Zhou Yu; Meng Wang; Jun Yu; | cvpr | 2023-05-17 |
1037 | Learning Situation Hyper-Graphs for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose an architecture for Video Question Answering (VQA) that enables answering questions related to video content by predicting situation hyper-graphs, coined Situation Hyper-Graph based Video Question Answering (SHG-VQA). |
AISHA UROOJ et. al. | cvpr | 2023-05-17 |
1038 | Discovering The Real Association: Multimodal Causal Reasoning in Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our work, we investigate relational structure from a causal representation perspective on multimodal data and propose a novel inference framework. |
Chuanqi Zang; Hanqing Wang; Mingtao Pei; Wei Liang; | cvpr | 2023-05-17 |
1039 | Are Deep Neural Networks SMARTer Than Second Graders? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Such a dramatic progress raises the question: how generalizable are neural networks in solving problems that demand broad skills? To answer this question, we propose SMART: a Simple Multimodal Algorithmic Reasoning Task and the associated SMART-101 dataset, for evaluating the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed specifically for children in the 6–8 age group. |
Anoop Cherian; Kuan-Chuan Peng; Suhas Lohit; Kevin A. Smith; Joshua B. Tenenbaum; | cvpr | 2023-05-17 |
1040 | From Images to Textual Prompts: Zero-Shot Visual Question Answering With Frozen Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: End-to-end training on vision and language data may bridge the disconnections, but is inflexible and computationally expensive. To address this issue, we propose Img2Prompt, a plug-and-play module that provides the prompts that can bridge the aforementioned modality and task disconnections, so that LLMs can perform zero-shot VQA tasks without end-to-end training. |
JIAXIAN GUO et. al. | cvpr | 2023-05-17 |
1041 | VQACL: A Novel Visual Question Answering Continual Learning Setting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we establish a novel VQA Continual Learning setting named VQACL, which contains two key components: a dual-level task sequence where visual and linguistic data are nested, and a novel composition testing containing new skill-concept combinations. |
Xi Zhang; Feifei Zhang; Changsheng Xu; | cvpr | 2023-05-17 |
1042 | RMLVQA: A Margin Loss Approach for Visual Question Answering With Language Biases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We address this through the second component, where instance-specific margins are learnt, allowing the model to distinguish between samples of varying complexity. We introduce a bias-injecting component to our model, and compute the instance-specific margins from the confidence of this component. |
Abhipsa Basu; Sravanti Addepalli; R. Venkatesh Babu; | cvpr | 2023-05-17 |
1043 | VindLU: A Recipe for Effective Video-and-Language Pretraining IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Hence, instead of proposing yet another new VidL model, this paper conducts a thorough empirical study demystifying the most important factors in the VidL model design. |
FENG CHENG et. al. | cvpr | 2023-05-17 |
1044 | An Empirical Study on The Language Modal in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We hope this study can inspire novel insights for future research on designing bias-reduction approaches. |
Daowan Peng; Wei Wei; Xian-Ling Mao; Yuanyuan Fu; Dangyang Chen; | arxiv-cs.AI | 2023-05-17 |
1045 | REVEAL: Retrieval-Augmented Visual-Language Pre-Training With Multi-Source Multimodal Knowledge Memory IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries. |
ZINIU HU et. al. | cvpr | 2023-05-17 |
1046 | ANetQA: A Large-Scale Benchmark for Fine-Grained Compositional Reasoning Over Untrimmed Videos Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we present ANetQA, a large-scale benchmark that supports fine-grained compositional reasoning over the challenging untrimmed videos from ActivityNet. |
ZHOU YU et. al. | cvpr | 2023-05-17 |
1047 | Clover: Towards A Unified Video-Language Alignment and Fusion Model IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Though offering attractive generality, the resulted models have to compromise between efficiency and performance. They mostly adopt different architectures to deal with different downstream tasks. We find this is because the pair-wise training cannot well align and fuse features from different modalities. We then introduce Clover–a Correlated Video-Language pre-training method–towards a universal video-language model for solving multiple video understanding tasks with neither performance nor efficiency compromise. |
JINGJIA HUANG et. al. | cvpr | 2023-05-17 |
1048 | PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we focus on the problem of Medical Visual Question Answering (MedVQA), which is crucial in efficiently interpreting medical images with vital clinic-relevant information. |
XIAOMAN ZHANG et. al. | arxiv-cs.CV | 2023-05-17 |
1049 | LAVENDER: Unifying Video-Language Understanding As Masked Language Modeling IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we explore a unified VidL framework LAVENDER, where Masked Language Modeling (MLM) is used as the common interface for all pre-training and downstream tasks. |
LINJIE LI et. al. | cvpr | 2023-05-17 |
1050 | Logical Implications for Visual Question Answering Consistency Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Instead, we propose a novel strategy intended to improve model performance by directly reducing logical inconsistencies. |
Sergio Tascon-Morales; Pablo Márquez-Neila; Raphael Sznitman; | cvpr | 2023-05-17 |
1051 | Generative Bias for Robust Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, in order to better learn the bias a target VQA model suffers from, we propose a generative method to train the bias model directly from the target model, called GenB. |
Jae Won Cho; Dong-Jin Kim; Hyeonggon Ryu; In So Kweon; | cvpr | 2023-05-17 |
1052 | MIST: Multi-Modal Iterative Spatial-Temporal Transformer for Long-Form Video Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a new model named Multi-modal Iterative Spatial-temporal Transformer (MIST) to better adapt pre-trained models for long-form VideoQA. |
DIFEI GAO et. al. | cvpr | 2023-05-17 |
1053 | XPQA: Cross-Lingual Product Question Answering Across 12 Languages Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While existing work on PQA focuses mainly on English, in practice there is need to support multiple customer languages while leveraging product information available in English. To study this practical industrial task, we present xPQA, a large-scale annotated cross-lingual PQA dataset in 12 languages across 9 branches, and report results in (1) candidate ranking, to select the best English candidate containing the information to answer a non-English question; and (2) answer generation, to generate a natural-sounding non-English answer based on the selected English candidate. |
Xiaoyu Shen; Akari Asai; Bill Byrne; Adrià de Gispert; | arxiv-cs.CL | 2023-05-16 |
1054 | Towards Expert-Level Medical Question Answering with Large Language Models IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this and other prior work suggested significant room for improvement, especially when models’ answers were compared to clinicians’ answers. Here we present Med-PaLM 2, which bridges these gaps by leveraging a combination of base LLM improvements (PaLM 2), medical domain finetuning, and prompting strategies including a novel ensemble refinement approach. |
KARAN SINGHAL et. al. | arxiv-cs.CL | 2023-05-16 |
1055 | Question-Answering System Extracts Information on Injection Drug Use from Clinical Notes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Conclusions: Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care. |
MARIA MAHBUB et. al. | arxiv-cs.AI | 2023-05-15 |
1056 | Is A Video Worth $n\times N$ Images? A Highly Efficient Approach to Transformer-based Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a highly efficient approach for VideoQA based on existing vision-language pre-trained models where we concatenate video frames to a $n\times n$ matrix and then convert it to one image. |
Chenyang Lyu; Tianbo Ji; Yvette Graham; Jennifer Foster; | arxiv-cs.CV | 2023-05-15 |
1057 | MeeQA: Natural Questions in Meeting Transcripts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present MeeQA, a dataset for natural-language question answering over meeting transcripts. |
Reut Apel; Tom Braude; Amir Kantor; Eyal Kolman; | arxiv-cs.CL | 2023-05-15 |
1058 | KEPR: Knowledge Enhancement and Plausibility Ranking for Generative Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Meanwhile, learning to distinguish positive answers from negative ones potentially enhances the probabilistic estimation of plausibility, and accordingly, the plausibility-based ranking. Therefore, we propose a Knowledge Enhancement and Plausibility Ranking (KEPR) approach grounded on the Generate-Then-Rank pipeline architecture. |
Zhifeng Li; Bowei Zou; Yifan Fan; Yu Hong; | arxiv-cs.CL | 2023-05-15 |
1059 | Learning to Generalize for Cross-domain QA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Current synthesized data augmentation methods for QA are hampered by increased training costs. To address this issue, we propose a novel approach that combines prompting methods and linear probing then fine-tuning strategy, which does not entail additional cost. |
Yingjie Niu; Linyi Yang; Ruihai Dong; Yue Zhang; | arxiv-cs.CL | 2023-05-14 |
1060 | Distinguish Before Answer: Generating Contrastive Explanation As Knowledge for Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the challenge, we propose CPACE, a Concept-centric Prompt-bAsed Contrastive Explanation Generation model, which aims to convert obtained symbolic knowledge into a contrastive explanation for better distinguishing the differences among given candidates. |
QIANGLONG CHEN et. al. | arxiv-cs.CL | 2023-05-14 |
1061 | Semantic-aware Dynamic Retrospective-Prospective Reasoning for Event-level Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: There is need for using such semantic connections to facilitate complex reasoning across video frames. Therefore, we propose a semantic-aware dynamic retrospective-prospective reasoning approach for video-based question answering. |
Chenyang Lyu; Tianbo Ji; Yvette Graham; Jennifer Foster; | arxiv-cs.CV | 2023-05-13 |
1062 | SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose Self-labeled Counterfactuals for Extrapolating to Negative Examples (SCENE), an automatic method for synthesizing training data that greatly improves models’ ability to detect challenging negative examples. |
Deqing Fu; Ameya Godbole; Robin Jia; | arxiv-cs.CL | 2023-05-13 |
1063 | Answering Complex Questions Over Text By Hybrid Question Parsing and Execution Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The dominant paradigm of textual question answering systems is based on end-to-end neural networks, which excels at answering natural language questions but falls short on complex … |
YE LIU et. al. | ArXiv | 2023-05-12 |
1064 | Implications of Deep Circuits in Improving Quality of Quantum Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we have attempted to understand questions in a better way by using Quantum Machine Learning (QML). |
Pragya Katyayan; Nisheeth Joshi; | arxiv-cs.CL | 2023-05-12 |
1065 | Open-WikiTable: Dataset for Open Domain Question Answering with Complex Reasoning Over Table Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As each question is coupled with both textual answers and SQL queries, Open-WikiTable opens up a wide range of possibilities for future research, as both reader and parser methods can be applied. |
Sunjun Kweon; Yeonsu Kwon; Seonhee Cho; Yohan Jo; Edward Choi; | arxiv-cs.CL | 2023-05-12 |
1066 | A Memory Model for Question Answering from Streaming Data Supported By Rehearsal and Anticipation of Coreference Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Drawing inspiration from these, we propose a memory model that performs rehearsal and anticipation while processing inputs to memorize important information for solving question answering tasks from streaming data. |
Vladimir Araujo; Alvaro Soto; Marie-Francine Moens; | arxiv-cs.CL | 2023-05-12 |
1067 | HPE:Answering Complex Questions Over Text By Hybrid Question Parsing and Execution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Towards combining the strengths of neural and symbolic methods, we propose a framework of question parsing and execution on textual QA. |
YE LIU et. al. | arxiv-cs.CL | 2023-05-12 |
1068 | Think Twice: Measuring The Efficiency of Eliminating Prediction Shortcuts of Question Answering Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a simple method for measuring a scale of models’ reliance on any identified spurious feature and assess the robustness towards a large set of known and newly found prediction biases for various pre-trained models and debiasing methods in Question Answering (QA). |
Lukáš Mikula; Michal Štefánik; Marek Petrovič; Petr Sojka; | arxiv-cs.CL | 2023-05-11 |
1069 | Overinformative Question Answering By Humans and Machines Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: But what principles guide the selection of additional information? In this paper, we provide experimental evidence from two studies suggesting that overinformativeness in human answering is driven by considerations of relevance to the questioner’s goals which they flexibly adjust given the functional context in which the question is uttered. |
Polina Tsvilodub; Michael Franke; Robert D. Hawkins; Noah D. Goodman; | arxiv-cs.CL | 2023-05-11 |
1070 | When Giant Language Brains Just Aren’t Enough! Domain Pizzazz with Knowledge Sparkle Dust Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents an empirical analysis aimed at bridging the gap in adapting LLMs to practical use cases. |
MINH-TIEN NGUYEN et. al. | arxiv-cs.CL | 2023-05-11 |
1071 | Evaluating Open-Domain Question Answering in The Era of Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we conduct a thorough analysis of various open-domain QA models, including LLMs, by manually evaluating their answers on a subset of NQ-open, a popular benchmark. |
Ehsan Kamalloo; Nouha Dziri; Charles L. A. Clarke; Davood Rafiei; | arxiv-cs.CL | 2023-05-11 |
1072 | Self-Chained Image-Language Model for Video Localization and Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although humans often find a video moment to focus on and rewind the moment to answer questions, training a query-aware video moment localizer often requires expensive annotations and high computational costs. To address this issue, we propose Self-Chained Video Localization-Answering (SeViLA), a novel framework that leverages a single image-language model (BLIP-2) to tackle both temporal keyframe localization and QA on videos. |
Shoubin Yu; Jaemin Cho; Prateek Yadav; Mohit Bansal; | arxiv-cs.CV | 2023-05-11 |
1073 | AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While previous datasets have focused primarily on languages where cross-lingual QA augments coverage from the target language, AfriQA focuses on languages where cross-lingual answer content is the only high-coverage source of answer content. Because of this, we argue that African languages are one of the most important and realistic use cases for XOR QA. |
ODUNAYO OGUNDEPO et. al. | arxiv-cs.CL | 2023-05-11 |
1074 | Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing studies primarily rest on grasping either unstructured evidence or potential reasoning paths from structured knowledge bases, yet failing to exploit the benefits of heterogeneous knowledge simultaneously. In light of this, we propose Decker, a commonsense fact verification model that is capable of bridging heterogeneous knowledge by uncovering latent relationships between structured and unstructured knowledge. |
Anni Zou; Zhuosheng Zhang; Hai Zhao; | arxiv-cs.CL | 2023-05-10 |
1075 | Multi-hop Commonsense Knowledge Injection Framework for Zero-Shot Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel multi-hop commonsense knowledge injection framework. |
XIN GUAN et. al. | arxiv-cs.CL | 2023-05-10 |
1076 | Large Language Models Need Holistically Thought in Medical Conversational QA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This is because medical CQA tasks require not only strong medical reasoning, but also the ability to think broadly and deeply. In this paper, to address these challenges in medical CQA tasks that need to be considered and understood in many aspects, we propose the Holistically Thought (HoT) method, which is designed to guide the LLMs to perform the diffused and focused thinking for generating high-quality medical responses. |
YIXUAN WENG et. al. | arxiv-cs.CL | 2023-05-09 |
1077 | MAUPQA: Massive Automatically-created Polish Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, manually annotating such datasets is both difficult and time-consuming, which limits their availability for less popular languages. In this work, we experiment with several methods for automatically collecting weakly labeled datasets and show how they affect the performance of the neural passage retrieval models. |
Piotr Rybak; | arxiv-cs.CL | 2023-05-09 |
1078 | Event Knowledge Incorporation with Posterior Regularization for Event-Centric Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a simple yet effective strategy to incorporate event knowledge extracted from event trigger annotations via posterior regularization to improve the event reasoning capability of mainstream question-answering (QA) models for event-centric QA. |
Junru Lu; Gabriele Pergola; Lin Gui; Yulan He; | arxiv-cs.CL | 2023-05-08 |
1079 | SkillQG: Learning to Generate Question for Reading Comprehension Assessment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present $\textbf{$\texttt{SkillQG}$}$: a question generation framework with controllable comprehension types for assessing and improving machine reading comprehension models. |
Xiaoqiang Wang; Bang Liu; Siliang Tang; Lingfei Wu; | arxiv-cs.CL | 2023-05-08 |
1080 | Visual Causal Scene Refinement for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, to discover critical video segments and frames that serve as the visual causal scene for generating reliable answers, we present a causal analysis of VideoQA and propose a framework for cross-modal causal relational reasoning, named Visual Causal Scene Refinement (VCSR). |
Yushen Wei; Yang Liu; Hong Yan; Guanbin Li; Liang Lin; | arxiv-cs.CV | 2023-05-07 |
1081 | FACTIFY-5WQA: 5W Aspect-based Fact Verification Through Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a 5W framework (who, what, when, where, and why) for question-answer-based fact explainability. |
ANKU RANI et. al. | arxiv-cs.CL | 2023-05-07 |
1082 | Adaptive Loose Optimization for Robust Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a simple yet effective novel loss function with adaptive loose optimization, which seeks to make the best of both worlds for question answering. |
JIE MA et. al. | arxiv-cs.CL | 2023-05-06 |
1083 | OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in Vietnamese Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce the OpenViVQA (Open-domain Vietnamese Visual Question Answering) dataset, the first large-scale dataset for VQA with open-ended answers in Vietnamese, consists of 11,000+ images associated with 37,000+ question-answer pairs (QAs). |
Nghia Hieu Nguyen; Duong T. D. Vo; Kiet Van Nguyen; Ngan Luu-Thuy Nguyen; | arxiv-cs.CL | 2023-05-06 |
1084 | T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning Via Large Language Model Signals for Science Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, the annotated rationales are hardly accurate due to the external essential information missed. To address these issues, we propose a novel method termed \emph{T-SciQ} that aims at teaching science question answering with LLM signals. |
LEI WANG et. al. | arxiv-cs.CL | 2023-05-05 |
1085 | Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, encoders rely more on Machine Reading Comprehension (MRC) methods, which take table serialization and text splicing as input, damaging the granularity relationship between table and text as well as the spatial structure information of table itself. In order to solve these problems, the paper proposes a Multi-View Graph (MVG) Encoder to take the relations among the granularity into account and capture the relations from multiple view. |
Yifan Wei; Fangyu Lei; Yuanzhe Zhang; Jun Zhao; Kang Liu; | arxiv-cs.CL | 2023-05-05 |
1086 | VideoOFA: Two-Stage Pre-Training for Video-to-Text Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result, our VideoOFA model achieves new state-of-the-art performance on four Video Captioning benchmarks, beating prior art by an average of 9.7 points in CIDEr score. |
XILUN CHEN et. al. | arxiv-cs.CV | 2023-05-04 |
1087 | Pay More Attention to Relation Exploration for Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a novel framework, RE-KBQA, that utilizes relations in the knowledge base to enhance entity representation and introduce additional supervision. |
YONG CAO et. al. | arxiv-cs.CL | 2023-05-03 |
1088 | NorQuAD: Norwegian Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper we present NorQuAD: the first Norwegian question answering dataset for machine reading comprehension. |
Sardana Ivanova; Fredrik Aas Andreassen; Matias Jentoft; Sondre Wold; Lilja Øvrelid; | arxiv-cs.CL | 2023-05-03 |
1089 | AttenWalker: Unsupervised Long-Document Question Answering Via Attention-based Graph Walking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Besides, we propose AttenWalker, a novel unsupervised method to aggregate and generate answers with long-range dependency so as to construct long-document QA pairs. |
Yuxiang Nie; Heyan Huang; Wei Wei; Xian-Ling Mao; | arxiv-cs.CL | 2023-05-03 |
1090 | Few-shot In-context Learning for Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To handle questions over diverse KBQA datasets with a unified training-free framework, we propose KB-BINDER, which for the first time enables few-shot in-context learning over KBQA tasks. |
TIANLE LI et. al. | arxiv-cs.CL | 2023-05-02 |
1091 | Huatuo-26M, A Large-scale Chinese Medical QA Dataset IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we release a largest ever medical Question Answering (QA) dataset with 26 million QA pairs. |
JIANQUAN LI et. al. | arxiv-cs.CL | 2023-05-02 |
1092 | FeQA: Fusion and Enhancement of Multi-source Knowledge on Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Jiahao Zhang; Bo Huang; Hamido Fujita; Guohui Zeng; Jin Liu; | Expert Syst. Appl. | 2023-05-01 |
1093 | CHIC: Corporate Document for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose CHIC a visual question-answering public dataset. |
Ibrahim Souleiman Mahamoud; Mickael Coustaty; Aurelie Joseph; Vincent Poulain d Andecy; Jean-Marc Ogier; | arxiv-cs.DB | 2023-05-01 |
1094 | Ask and Ye Shall Be Answered: Bayesian Tag-based Collaborative Recommendation of Trustworthy Experts Over Time in Community Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
G. Costa; R. Ortale; | Inf. Fusion | 2023-05-01 |
1095 | SPARQL-QA-v2 System for Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
M. Santana; F. Ricca; | Expert Syst. Appl. | 2023-05-01 |
1096 | Joint Reasoning with Knowledge Subgraphs for Multiple Choice Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Qin Zhang; Shan Chen; Menglu Fang; Xiaojun Chen; | Inf. Process. Manag. | 2023-05-01 |
1097 | SoarGraph: Numerical Reasoning Over Financial Table-Text Data Via Semantic-Oriented Hierarchical Graphs Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Towards the intelligent understanding of table-text data in the finance domain, previous research explores numerical reasoning over table-text content with Question Answering (QA) … |
FENGBIN ZHU et. al. | Companion Proceedings of the ACM Web Conference 2023 | 2023-04-30 |
1098 | Temporal Ordinance Mining for Event-Driven Social Media Reaction Analytics Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: As a growing number of policies are adopted to address the substantial rise in urbanization, there is a significant push for smart governance, endowing transparency in … |
A. Varde; Gerard de Melo; Boxiang Dong; | Companion Proceedings of the ACM Web Conference 2023 | 2023-04-30 |
1099 | Multimodal Graph Transformer for Multimodal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we aim to benefit from both worlds and propose a novel Multimodal Graph Transformer for question answering tasks that requires performing reasoning across multiple modalities. |
Xuehai He; Xin Eric Wang; | arxiv-cs.CV | 2023-04-30 |
1100 | TML: A Temporal-aware Multitask Learning Framework for Time-sensitive Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Many facts change over time, Time-sensitive Question Answering(TSQA) answers questions about time evolution facts to test the model’s ability in the dimension of the time. The … |
Ziqiang Chen; Shaojuan Wu; Xiaowang Zhang; Zhiyong Feng; | Companion Proceedings of the ACM Web Conference 2023 | 2023-04-30 |
1101 | Query-Driven Knowledge Graph Construction Using Question Answering and Multimodal Fusion Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Over recent years, large knowledge bases have been constructed to store massive knowledge graphs. However, these knowledge graphs are highly incomplete. To solve this problem, we … |
Yang Peng; | Companion Proceedings of the ACM Web Conference 2023 | 2023-04-30 |
1102 | Hierarchy-Aware Multi-Hop Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose HamQA, a novel Hierarchy-aware multi-hop Question Answering framework on knowledge graphs, to effectively align the mutual hierarchical information between question contexts and KGs. |
JUNNAN DONG et. al. | www | 2023-04-29 |
1103 | Knowledge Graph Question Answering with Ambiguous Query Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose PReFNet which focuses on answering ambiguous queries with pseudo relevance feedback on knowledge graphs. |
Lihui Liu; Yuzhong Chen; Mahashweta Das; Hao Yang; Hanghang Tong; | www | 2023-04-29 |
1104 | MSQ-BioBERT: Ambiguity Resolution to Enhance BioBERT Medical Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduced a novel approach called the Multiple Synonymous Questions BioBERT (MSQ-BioBERT), which integrates question augmentation, rather than the typical single question used by traditional BioBERT, to elevate BioBERT’s performance on medical QA tasks. |
Muzhe Guo; Muhao Guo; Edward T. Dougherty; Fang Jin; | www | 2023-04-29 |
1105 | Learning to Build Reasoning Chains By Reliable Path Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a ReliAble Path-retrieval (RAP) to generate varying length evidence chains iteratively. |
M. ZHU et. al. | icassp | 2023-04-27 |
1106 | Choice Fusion As Knowledge For Zero-Shot Dialogue State Tracking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although prior works have leveraged question-answering (QA) data to reduce the need for in-domain training in DST, they fail to explicitly model knowledge transfer and fusion for tracking dialogue states. To address this issue, we propose CoFunDST, which is trained on domain-agnostic QA datasets and directly uses candidate choices of slot-values as knowledge for zero-shot dialogue-state generation, based on a T5 pre-trained language model. |
R. Su; J. Yang; T. -W. Wu; B. -H. Juang; | icassp | 2023-04-27 |
1107 | SADE: A Self-Adaptive Expert for Multi-Dataset Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This approach, however, has its limitations when generalized to an unseen new distribution, and the number of extra parameters will increase with the number of training datasets. In this paper, we devise Self-ADaptive Expert (SADE), the key idea of which is to train a single expert that can be automatically adapted to each individual instance according to its gradients. |
Y. Peng; Q. Wang; Z. Mao; Y. Zhang; | icassp | 2023-04-27 |
1108 | Outside Knowledge Visual Question Answering Version 2.0 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper describes the analysis, corrections, and removals completed and presents a new dataset: OK-VQA Version 2.0. |
B. Z. REICHMAN et. al. | icassp | 2023-04-27 |
1109 | An Interpretable Model Using Evidence Information for Multi-Hop Question Answering Over Long Texts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To better use evidence information, we propose a loss function considering answer groups, which improves the reasoning ability of the reader in the Retriever-Reader architecture. |
Y. Chen; R. Liu; X. Liu; Y. Shi; G. Bai; | icassp | 2023-04-27 |
1110 | Narrow Down Before Selection: A Dynamic Exclusion Model for Multiple-Choice QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a dynamic exclusion model for MCQA named ExcMC, which mimics human thinking in selection. |
X. Liu; Y. Shi; R. Liu; G. Bai; Y. Chen; | icassp | 2023-04-27 |
1111 | From Easy to Hard: Two-Stage Selector and Reader for Multi-Hop Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing works commonly introduce techniques such as graph modeling and question decomposition to explore precise intermediate results of multi-hop reasoning, leading to complexity growth and error accumulation. In this paper, we propose FE2H, a simple yet effective framework without extra tasks to address these problems. |
X. -Y. Li; W. -J. Lei; Y. -B. Yang; | icassp | 2023-04-27 |
1112 | Self-Adaptive Reasoning on Sub-Questions for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present the Self-Adapting Reasoning Model (SAR) for solving multi-hop question answering (MHQA) tasks, where the QA system is supposed to find the correct answer within the given multiple documents and a multi-hop question. |
Z. Li; W. Peng; | icassp | 2023-04-27 |
1113 | Time-Aware Multiway Adaptive Fusion Network for Temporal Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most of existing methods are developed based on pre-trained language models, which might not be capable to learn temporal-specific presentations of entities in terms of temporal KGQA task. To alleviate this problem, we propose a novel Time-aware Multiway Adaptive (TMA) fusion network. |
Y. LIU et. al. | icassp | 2023-04-27 |
1114 | Nested Attention Network with Graph Filtering for Visual Question and Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Recently, Visual Question Answering(VQA), which is required to generate the answer by understanding both visual and textual content, has attracted considerable research interest. … |
J. Lu; C. Wu; L. Wang; S. Yuan; J. Wu; | icassp | 2023-04-27 |
1115 | Image Generation Is May All You Need for VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, image generation of VQA has been implemented in a limited way to modify only certain parts of the original image in order to control the quality and uncertainty. In this paper, to address this gap, we propose a method that utilizes the diffusion model, pre-trained with various tasks and images, to inject the prior knowledge base into generated images and secure diversity without losing generality about the answer. |
K. Kim; J. Lee; J. Lee; | icassp | 2023-04-27 |
1116 | Confidence-Based Event-Centric Online Video Question Answering on A Newly Constructed ATBS Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the challenges of VideoQA on long videos of unknown length, we define a new set of problems called Online Open-ended Video Question Answering (O2VQA). |
W. Kong; S. Ye; C. Yao; J. Ren; | icassp | 2023-04-27 |
1117 | Answering Uncertain, Under-Specified API Queries Assisted By Knowledge-Aware Human-AI Dialogue Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper designs a novel Knowledge-Aware Human-AI Dialog agent (KAHAID) which guides the developer to clarify the uncertain, under-specified query through multi-round question answering and recommends APIs for the clarified query with relevance explanation and extended suggestions (e.g., alternative, collaborating or opposite-function APIs). |
QING HUANG et. al. | arxiv-cs.SE | 2023-04-27 |
1118 | Question Answering System with Sparse and Noisy Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by a practical need in Question Answering System of processing these two types of rewards, this paper investigates and proposes a new stochastic multi-armed bandit model in which each action has a noisy reward and a sparse reward. We studied this problem in the contextual bandit settings, and proposed and analyzed efficient algorithms that are based on the LINUCB frameworks. |
D. Bouneffouf; O. Alkan; R. Feraud; B. Lin; | icassp | 2023-04-27 |
1119 | Q2d: Turning Questions Into Dialogs to Teach Models How to Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose q2d: an automatic data generation pipeline that generates information-seeking dialogs from questions. |
YONATAN BITTON et. al. | arxiv-cs.CL | 2023-04-27 |
1120 | PMC-LLaMA: Towards Building Open-source Language Models for Medicine IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we describe the procedure for building a powerful, open-source language model specifically designed for medicine applications, termed as PMC-LLaMA. |
CHAOYI WU et. al. | arxiv-cs.CL | 2023-04-27 |
1121 | Source-Free Unsupervised Domain Adaptation for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce Source-Free Domain Adaptation Framework for QA (denoted as SFQA), which only allows access to trained source models for target learning, making data privacy protection more promising. |
Z. ZHAO et. al. | icassp | 2023-04-27 |
1122 | HeySQuAD: A Spoken Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This study presents a new large-scale community-shared SQA dataset called HeySQuAD, which includes 76k human-spoken questions, 97k machine-generated questions, and their corresponding textual answers from the SQuAD QA dataset. |
YIJING WU et. al. | arxiv-cs.CL | 2023-04-26 |
1123 | Unstructured and Structured Data: Can We Have The Best of Both Worlds with Large Language Models? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents an opinion on the potential of using large language models to query on both unstructured and structured data. |
Wang-Chiew Tan; | arxiv-cs.DB | 2023-04-25 |
1124 | Better Question-Answering Models on A Budget Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present Eluwa, a family of LoRA models that use the Stanford Alpaca dataset and massively improve the capabilities of Facebook’s OPT 1.3B, 2.7B and 6.7B models. |
Yudhanjaya Wijeratne; Ishan Marikar; | arxiv-cs.CL | 2023-04-24 |
1125 | PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work proposes a synthetic data generation method for cross-lingual QA which leverages indirect supervision from existing parallel corpora. |
Bryan Li; Chris Callison-Burch; | arxiv-cs.CL | 2023-04-24 |
1126 | Extreme Classification for Answer Type Prediction in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose use of extreme multi-label classification using Transformer models (XBERT) by clustering KG types using structural and semantic features based on question text. |
Vinay Setty; | arxiv-cs.CL | 2023-04-24 |
1127 | Unlocking Context Constraints of LLMs: Enhancing Context Efficiency of LLMs with Self-Information-Based Content Filtering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a method called \textit{Selective Context} that employs self-information to filter out less informative content, thereby enhancing the efficiency of the fixed context length. |
Yucheng Li; | arxiv-cs.CL | 2023-04-24 |
1128 | IslamicPCQA: A Dataset for Persian Multi-hop Complex Question Answering in Islamic Text Resources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this article, the IslamicPCQA dataset is introduced. |
Arash Ghafouri; Hasan Naderi; Mohammad Aghajani asl; Mahdi Firouzmandi; | arxiv-cs.CL | 2023-04-23 |
1129 | A Review of Deep Learning for Video Captioning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This survey covers deep learning-based VC, including but, not limited to, attention-based architectures, graph networks, reinforcement learning, adversarial networks, dense video captioning (DVC), and more. |
MOLOUD ABDAR et. al. | arxiv-cs.CV | 2023-04-22 |
1130 | Information Extraction from Documents: Question Answering Vs Token Classification in Real-world Setups Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we compare the Question Answering approach with the classical token classification approach for document key information extraction. |
Laurent Lam; Pirashanth Ratnamogan; Joël Tang; William Vanhuffel; Fabien Caspani; | arxiv-cs.CL | 2023-04-21 |
1131 | Tokenization Preference for Human and Machine Learning Model: An Annotation Study Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study examines the relations between preferred tokenization for humans (appropriateness and readability) and one for ML models (performance on an NLP task). |
Tatsuya Hiraoka; Tomoya Iwakura; | arxiv-cs.CL | 2023-04-21 |
1132 | Why Does ChatGPT Fall Short in Providing Truthful Answers? IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Through experiments focusing on factuality, we propose several potential enhancement strategies. |
Shen Zheng; Jie Huang; Kevin Chen-Chuan Chang; | arxiv-cs.CL | 2023-04-20 |
1133 | BRENT: Bidirectional Retrieval Enhanced Norwegian Transformer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We develop the first Norwegian retrieval-based model by adapting the REALM framework and evaluating it on various tasks. |
Lucas Georges Gabriel Charpentier; Sondre Wold; David Samuel; Egil Rønningstad; | arxiv-cs.CL | 2023-04-19 |
1134 | Exploring Chart Question Answering for Blind and Low Vision Users IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Data visualizations can be complex or involve numerous data points, making them impractical to navigate using screen readers alone. Question answering (QA) systems have the … |
Jiho Kim; Arjun Srinivasan; N. Kim; Yea-Seul Kim; | Proceedings of the 2023 CHI Conference on Human Factors in … | 2023-04-19 |
1135 | SurgicalGPT: End-to-End Language-Vision GPT for Visual Question Answering in Surgery Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As GPT does not natively process vision tokens, to exploit the advancements in GPT models for VQA in robotic surgery, we design an end-to-end trainable Language-Vision GPT (LV-GPT) model that expands the GPT2 model to include vision input (image). |
Lalithkumar Seenivasan; Mobarakol Islam; Gokul Kannan; Hongliang Ren; | arxiv-cs.CV | 2023-04-19 |
1136 | LLM As A Robotic Brain: Unifying Egocentric Memory and Control IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel and generalizable framework called LLM-Brain: using Large-scale Language Model as a robotic brain to unify egocentric memory and control. |
JINJIE MAI et. al. | arxiv-cs.AI | 2023-04-18 |
1137 | In ChatGPT We Trust? Measuring and Characterizing The Reliability of ChatGPT IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we perform the first large-scale measurement of ChatGPT’s reliability in the generic QA scenario with a carefully curated set of 5,695 questions across ten datasets and eight domains. |
Xinyue Shen; Zeyuan Chen; Michael Backes; Yang Zhang; | arxiv-cs.CR | 2023-04-18 |
1138 | Learning Situation Hyper-Graphs for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose an architecture for Video Question Answering (VQA) that enables answering questions related to video content by predicting situation hyper-graphs, coined Situation Hyper-Graph based Video Question Answering (SHG-VQA). |
AISHA UROOJ KHAN et. al. | arxiv-cs.CV | 2023-04-17 |
1139 | Keeping The Questions Conversational: Using Structured Representations to Resolve Dependency in Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework, CONVSR (CONVQA using Structured Representations) for capturing and generating intermediate representations as conversational cues to enhance the capability of the QA model to better interpret the incomplete questions. |
Munazza Zaib; Quan Z. Sheng; Wei Emma Zhang; Adnan Mahmood; | arxiv-cs.CL | 2023-04-14 |
1140 | Medical Question Summarization with Entity-driven Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although existing works have attempted to utilize Seq2Seq, reinforcement learning, or contrastive learning to solve the problem, two challenges remain: how to correctly capture question focus to model its semantic intention, and how to obtain reliable datasets to fairly evaluate performance. To address these challenges, this paper proposes a novel medical question summarization framework using entity-driven contrastive learning (ECL). |
SIBO WEI et. al. | arxiv-cs.CL | 2023-04-14 |
1141 | PDFVQA: A New Dataset for Real-World VQA on PDF Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We proposed a new document-based VQA dataset, PDF-VQA, to comprehensively examine the document understanding from various aspects, including document element recognition, document layout structural understanding as well as contextual understanding and key information extraction. |
Yihao Ding; Siwen Luo; Hyunsuk Chung; Soyeon Caren Han; | arxiv-cs.CV | 2023-04-13 |
1142 | Exploring The State of The Art in Legal QA Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address this problem, we provide a comprehensive survey that reviews 14 benchmark datasets for question-answering in the legal field as well as presents a comprehensive review of the state-of-the-art Legal Question Answering deep learning models. We cover the different architectures and techniques used in these studies and the performance and limitations of these models. |
Abdelrahman Abdallah; Bhawna Piryani; Adam Jatowt; | arxiv-cs.CL | 2023-04-13 |
1143 | CLIP-Guided Vision-Language Pre-training for Question Answering in 3D Scenes IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Training models to apply linguistic knowledge and visual concepts from 2D images to 3D world understanding is a promising direction that researchers have only recently started to … |
MARIA PARELLI et. al. | 2023 IEEE/CVF Conference on Computer Vision and Pattern … | 2023-04-12 |
1144 | ChatClimate: Grounding Conversational AI in Climate Science IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome these barriers, one potential solution is to provide LLMs with access to external, scientifically accurate, and robust sources (long-term memory) to continuously update their knowledge and prevent the propagation of inaccurate, incorrect, or outdated information. In this study, we enhanced GPT-4 by integrating the information from the Sixth Assessment Report of the Intergovernmental (IPCC AR6), the most comprehensive, up-to-date, and reliable source in this domain. |
SAEID ASHRAF VAGHEFI et. al. | arxiv-cs.CL | 2023-04-11 |
1145 | FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical Domain IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. |
YANIS LABRAK et. al. | arxiv-cs.CL | 2023-04-09 |
1146 | Multilingual Augmentation for Robust Visual Question Answering in Remote Sensing Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With the proposed augmented dataset, we are able to obtain more questions in addition to the original ones with the same meaning. To make better use of this information, in this study, we propose a contrastive learning strategy for training robust RSVQA models against diverse question templates and words. |
Zhenghang Yuan; Lichao Mou; Xiao Xiang Zhu; | arxiv-cs.CV | 2023-04-07 |
1147 | Language Models Are Causal Knowledge Extractors for Zero-shot Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, QG models only learn to ask association questions (e.g., “what is someone doing…”) and result in inferior performance due to the poor transfer of association knowledge to CVidQA, which focuses on causal questions like “why is someone doing …”. Observing this, we proposed to exploit causal knowledge to generate question-answer pairs, and proposed a novel framework, Causal Knowledge Extraction from Language Models (CaKE-LM), leveraging causal commonsense knowledge from language models to tackle CVidQA. |
Hung-Ting Su; Yulei Niu; Xudong Lin; Winston H. Hsu; Shih-Fu Chang; | arxiv-cs.CL | 2023-04-07 |
1148 | Bridging The Language Gap: Knowledge Injected Multilingual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a generalized cross-lingual transfer framework to enhance the model’s ability to understand different languages. |
ZHICHAO DUAN et. al. | arxiv-cs.CL | 2023-04-06 |
1149 | Improving Visual Question Answering Models Through Robustness Analysis and In-Context Learning with A Chain of Basic Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work proposes a new method that utilizes semantically related questions, referred to as basic questions, acting as noise to evaluate the robustness of VQA models. |
Jia-Hong Huang; Modar Alfadly; Bernard Ghanem; Marcel Worring; | arxiv-cs.CV | 2023-04-06 |
1150 | Evidentiality-aware Retrieval for Overcoming Abstractiveness in Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we instead focus on a data-centric approach and propose Evidentiality-Aware Dense Passage Retrieval (EADPR), which leverages synthetic distractor samples to learn to discriminate evidence passages from distractors. |
YONGHO SONG et. al. | arxiv-cs.AI | 2023-04-06 |
1151 | Evaluating The Robustness of Machine Reading Comprehension Models to Low Resource Entity Renaming Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we explore the robustness of MRC models to entity renaming, with entities from low-resource regions such as Africa. |
Clemencia Siro; Tunde Oluwaseyi Ajayi; | arxiv-cs.CL | 2023-04-06 |
1152 | Ericson: An Interactive Open-Domain Conversational Search Agent Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a fully functional ODCS system, Ericson, which includes state-of-the-art question answering and information retrieval components, as well as intent inference and dialogue management models for proactive question refinement and recommendations. |
Zihao Wang; Ali Ahmadvand; Jason Choi; Payam Karisani; Eugene Agichtein; | arxiv-cs.CL | 2023-04-05 |
1153 | Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This study investigates the performance of LLMs such as the ChatGPT family of models (GPT-3.5s, GPT-4) in biomedical tasks beyond question-answering. |
SHAN CHEN et. al. | arxiv-cs.CL | 2023-04-05 |
1154 | SC-ML: Self-supervised Counterfactual Metric Learning for Debiased Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We tackle the language bias problem by proposing a self-supervised counterfactual metric learning (SC-ML) method to focus the image features better. |
XINYAO SHU et. al. | arxiv-cs.CV | 2023-04-04 |
1155 | Instance-Level Trojan Attacks on Visual Question Answering Via Adversarial Learning in Neuron Activation Space Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose an instance-level multimodal Trojan attack on VQA that efficiently adapts to fine-tuned models through a dual-modality adversarial learning method. |
Yuwei Sun; Hideya Ochiai; Jun Sakuma; | arxiv-cs.CV | 2023-04-01 |
1156 | Dual-decoder Transformer Network for Answer Grounding in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Liangjun Zhu; Li Peng; Weinan Zhou; Jielong Yang; | Pattern Recognit. Lett. | 2023-04-01 |
1157 | Question-aware Dynamic Scene Graph of Local Semantic Representation Learning for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
JINMENG WU et. al. | Pattern Recognit. Lett. | 2023-04-01 |
1158 | Heterogeneous Question Answering Community Detection Based on Graph Neural Network IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
YONGLIANG WU et. al. | Inf. Sci. | 2023-04-01 |
1159 | MoCA: Incorporating Domain Pretraining and Cross Attention for Textbook Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
FANGZHI XU et. al. | Pattern Recognit. | 2023-04-01 |
1160 | WebQAmGaze: A Multilingual Webcam Eye-Tracking-While-Reading Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present WebQAmGaze, a multilingual low-cost eye-tracking-while-reading dataset, designed as the first webcam-based eye-tracking corpus of reading to support the development of explainable computational language processing models. |
Tiago Ribeiro; Stephanie Brandl; Anders Søgaard; Nora Hollenstein; | arxiv-cs.CL | 2023-03-31 |
1161 | Contrastive Refinement for Dense Retrieval Inference in The Open-Domain Question Answering Task Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In recent years, dense retrieval has emerged as the primary method for open-domain question-answering (OpenQA). However, previous research often focused on the query side, … |
Qiuhong Zhai; Wenhao Zhu; Xiaoyu Zhang; Chenyun Liu; | Future Internet | 2023-03-31 |
1162 | UKP-SQuARE V3: A Platform for Multi-Agent QA Research Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: A popular approach is to use multi-dataset models, which are models trained on multiple datasets to learn their regularities and prevent overfitting to a single dataset. |
HARITZ PUERTO et. al. | arxiv-cs.CL | 2023-03-31 |
1163 | Aligning A Medium-size GPT Model in English to A Small Closed Domain in Spanish Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a methodology to align a medium-sized GPT model, originally trained in English for an open domain, to a small closed domain in Spanish. |
Oscar R. Navarrete-Parra; Victor Uc-Cetina; Jorge Reyes-Magana; | arxiv-cs.CL | 2023-03-30 |
1164 | DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present dialog-enabled resolving agents (DERA). |
Varun Nair; Elliot Schumacher; Geoffrey Tso; Anitha Kannan; | arxiv-cs.CL | 2023-03-29 |
1165 | QaAskeR $$^+$$ + : A Novel Testing Method for Question Answering Software Via Asking Recursive Questions Related Papers Related Patents Related Grants Related Venues Related Experts View |
Xiaoyuan Xie; Shuo Jin; Songqiang Chen; | Automated Software Engineering | 2023-03-28 |
1166 | ChatGPT Is A Knowledgeable But Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we specifically focus on ChatGPT, a widely used and easily accessible LLM, and ask the following questions: (1) Can ChatGPT effectively answer commonsense questions? |
NING BIAN et. al. | arxiv-cs.CL | 2023-03-28 |
1167 | Curriculum Learning for Compositional Visual Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an NMN method that relies on predefined cross-modal embeddings to “warm start” learning on the GQA dataset, then focus on Curriculum Learning (CL) as a way to improve training and make a better use of the data. |
Wafa Aissa; Marin Ferecatu; Michel Crucianu; | arxiv-cs.CL | 2023-03-27 |
1168 | DBLP-QuAD: A Question Answering Dataset Over The DBLP Scholarly Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work we create a question answering dataset over the DBLP scholarly knowledge graph (KG). |
Debayan Banerjee; Sushil Awale; Ricardo Usbeck; Chris Biemann; | arxiv-cs.DL | 2023-03-23 |
1169 | GETT-QA: Graph Embedding Based T2T Transformer for Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present an end-to-end Knowledge Graph Question Answering (KGQA) system named GETT-QA. |
Debayan Banerjee; Pranav Ajit Nair; Ricardo Usbeck; Chris Biemann; | arxiv-cs.CL | 2023-03-23 |
1170 | GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address these, we propose GrapeQA with two simple improvements on the WG: (i) Prominent Entities for Graph Augmentation identifies relevant text chunks from the QA pair and augments the WG with corresponding latent representations from the LM, and (ii) Context-Aware Node Pruning removes nodes that are less relevant to the QA pair. |
DHAVAL TAUNK et. al. | arxiv-cs.CL | 2023-03-22 |
1171 | Integrating Image Features with Convolutional Sequence-to-sequence Network for Multilingual Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We approached the challenge as a sequence-to-sequence learning task, in which we integrated hints from pre-trained state-of-the-art VQA models and image features with Convolutional Sequence-to-Sequence network to generate the desired answers. |
Triet Minh Thai; Son T. Luu; | arxiv-cs.CV | 2023-03-22 |
1172 | LogQA: Question Answering in Unstructured Logs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose LogQA, which aims at answering log-based questions in the form of natural language based on large-scale unstructured log corpora. |
SHAOHAN HUANG et. al. | arxiv-cs.NI | 2023-03-21 |
1173 | TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on this approach, we introduce TIFA v1.0, a benchmark consisting of 4K diverse text inputs and 25K questions across 12 categories (object, counting, etc.). |
YUSHI HU et. al. | arxiv-cs.CV | 2023-03-21 |
1174 | FarsNewsQA: A Deep Learning-based Question Answering System for The Persian News Articles Related Papers Related Patents Related Grants Related Venues Related Experts View |
A. KAZEMI et. al. | Information Retrieval Journal | 2023-03-19 |
1175 | COVID-19 Event Extraction from Twitter Via Extractive Question Answering with Continuous Prompts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we cast the problem of event extraction as extractive question answering using recent advances in continuous prompting in language models. |
Yuhang Jiang; Ramakanth Kavuluru; | arxiv-cs.CL | 2023-03-19 |
1176 | FVQA 2.0: Introducing Adversarial Samples Into Fact-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It has been observed that the original dataset is highly imbalanced and concentrated on a small portion of its associated knowledge graph. We introduce FVQA 2.0 which contains adversarial variants of test questions to address this imbalance. |
Weizhe Lin; Zhilin Wang; Bill Byrne; | arxiv-cs.CL | 2023-03-19 |
1177 | A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we extend the scope to include a reasoner that constructs a question-dependent open knowledge graph based on retrieved supporting facts and employs a sequential subgraph reasoning process to predict the answer. |
Zhen Han; Yue Feng; Mingming Sun; | arxiv-cs.CL | 2023-03-18 |
1178 | An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we summarize two basic KGQA frameworks based on PLMs without additional neural network modules to compare the performance of nine PLMs in terms of accuracy and efficiency. |
NAN HU et. al. | arxiv-cs.CL | 2023-03-18 |
1179 | Personalized Course Resource Recommendation Algorithm Based on Deep Learning in The Intelligent Question Answering Robot Environment Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Aiming at the problems of difficult-to-extract effective information and insufficient feature extraction in the existing intelligent question answering robot environment, a … |
Peng Sun; | Int. J. Inf. Technol. Syst. Approach | 2023-03-17 |
1180 | Generate, Transform, Answer: Question Specific Tool Synthesis for Tabular Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unlike humans who use programmatic tools like filters to transform data before processing, language models in TQA process tables directly, resulting in information loss as table size increases. In this paper we propose ToolWriter to generate query specific programs and detect when to apply them to transform tables and align them with the TQA model’s capabilities. |
Carlos Gemmell; Jeffrey Dalton; | arxiv-cs.LG | 2023-03-17 |
1181 | A Quick Prototype for Assessing OpenIE Knowledge Graph-Based Question-Answering Systems IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Due to the rapid growth of knowledge graphs (KG) as representational learning methods in recent years, question-answering approaches have received increasing attention from … |
Giuseppina Di Paolo; Diego Rincon-Yanez; S. Senatore; | Inf. | 2023-03-16 |
1182 | Logical Implications for Visual Question Answering Consistency Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Instead, we propose a novel strategy intended to improve model performance by directly reducing logical inconsistencies. |
Sergio Tascon-Morales; Pablo Márquez-Neila; Raphael Sznitman; | arxiv-cs.CV | 2023-03-16 |
1183 | Secret-Keeping in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We seek to determine if it is possible to teach a question-answering system to keep a specific fact secret. |
Nathaniel W. Rollings; Kent O’Sullivan; Sakshum Kulshrestha; | arxiv-cs.CL | 2023-03-15 |
1184 | Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of The Question Answering Performance of The GPT LLM Family IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a framework that follows the black-box testing specifications of CheckList proposed by Ribeiro et. |
YIMING TAN et. al. | arxiv-cs.CL | 2023-03-14 |
1185 | Generating Multiple-choice Questions for Medical Question Answering with Distractors and Cue-masking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we show that (1) fine-tuning on generated MCQA dataset outperforms the masked language modeling based objective and (2) correctly masking the cues to the answers is critical for good performance. |
Damien Sileo; Kanimozhi Uma; Marie-Francine Moens; | arxiv-cs.CL | 2023-03-13 |
1186 | Polar-VQA: Visual Question Answering on Remote Sensed Ice Sheet Imagery from Polar Region Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we have introduced the task of Visual Question Answering (VQA) on remote-sensed ice sheet imagery. |
Argho Sarkar; Maryam Rahnemoonfar; | arxiv-cs.CV | 2023-03-13 |
1187 | MuLTI: Efficient Video-and-Language Understanding with Text-Guided MultiWay-Sampler and Multiple Choice Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes MuLTI, a highly accurate and efficient video-and-language understanding model that achieves efficient and effective feature fusion and rapid adaptation to downstream tasks. |
Jiaqi Xu; Bo Liu; Yunkuo Chen; Mengli Cheng; Xing Shi; | arxiv-cs.CV | 2023-03-10 |
1188 | Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Leveraging pre-trained language models, we introduce a novel method particularly suited for small, domain-specific, medical datasets. |
Tom van Sonsbeek; Mohammad Mahdi Derakhshani; Ivona Najdenkoska; Cees G. M. Snoek; Marcel Worring; | arxiv-cs.CV | 2023-03-10 |
1189 | Evaluating Question Generation Models Using QA Systems and Semantic Textual Similarity Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Question generation based on conversational context is a difficult problem to solve. A widely used technique for generating quality questions using fine-tuned models relies on a … |
Safwan Shaheer; Ishmam Hossain; Sudipta Nandi Sarna; Md Humaion Kabir Mehedi; Annajiat Alim Rasel; | 2023 IEEE 13th Annual Computing and Communication Workshop … | 2023-03-08 |
1190 | Interpretable Visual Question Answering Referring to Outside Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel multimodal interpretable VQA model that can answer the question more accurately and generate diverse explanations. |
He Zhu; Ren Togo; Takahiro Ogawa; Miki Haseyama; | arxiv-cs.CV | 2023-03-08 |
1191 | Graph Neural Networks in Vision-Language Image Understanding: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Graphs provide a natural way to represent the relational arrangement between objects in an image, and thus, in recent years graph neural networks (GNNs) have become a standard component of many 2D image understanding pipelines, becoming a core architectural component, especially in the VQA group of tasks. In this survey, we review this rapidly evolving field and we provide a taxonomy of graph types used in 2D image understanding approaches, a comprehensive list of the GNN models used in this domain, and a roadmap of future potential developments. |
Henry Senior; Gregory Slabaugh; Shanxin Yuan; Luca Rossi; | arxiv-cs.CV | 2023-03-07 |
1192 | Confidence-based Event-centric Online Video Question Answering on A Newly Constructed ATBS Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the challenges of VideoQA on long videos of unknown length, we define a new set of problems called Online Open-ended Video Question Answering (O^2VQA). |
Weikai Kong; Shuhong Ye; Chenglin Yao; Jianfeng Ren; | arxiv-cs.MM | 2023-03-06 |
1193 | AmQA: Amharic Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Hence, to foster the research in Amharic QA, we present the first Amharic QA (AmQA) dataset. |
Tilahun Abedissa; Ricardo Usbeck; Yaregal Assabie; | arxiv-cs.CL | 2023-03-06 |
1194 | Video Question Answering Using CLIP-Guided Visual-Text Attention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a visual-text attention mechanism to utilize the Contrastive Language-Image Pre-training (CLIP) trained on lots of general domain language-image pairs to guide the cross-modal learning for VideoQA. |
Shuhong Ye; Weikai Kong; Chenglin Yao; Jianfeng Ren; Xudong Jiang; | arxiv-cs.CV | 2023-03-06 |
1195 | VTQA: Visual Text Question Answering Via Entity Alignment and Cross-Media Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a new challenge with a dataset that contains 23,781 questions based on 10124 image-text pairs. |
Kang Chen; Xiangqian Wu; | arxiv-cs.CV | 2023-03-05 |
1196 | Knowledge-Based Counterfactual Queries for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a systematic method for explaining the behavior and investigating the robustness of VQA models through counterfactual perturbations. |
Theodoti Stoikou; Maria Lymperaiou; Giorgos Stamou; | arxiv-cs.CL | 2023-03-05 |
1197 | Domain Specific Question Answering Over Knowledge Graphs Using Logical Programming and Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Overall, our work presents a promising approach to addressing question answering over domain-specific graphs, offering an explainable and robust solution by incorporating logical programming languages. |
Navid Madani; Rohini K. Srihari; Kenneth Joseph; | arxiv-cs.LG | 2023-03-03 |
1198 | Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present Prophet — a conceptually simple, flexible, and general framework designed to prompt LLM with answer heuristics for knowledge-based VQA. |
Zhou Yu; Xuecheng Ouyang; Zhenwei Shao; Meng Wang; Jun Yu; | arxiv-cs.CV | 2023-03-03 |
1199 | MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose MixPHM, a redundancy-aware parameter-efficient tuning method that outperforms full finetuning in low-resource VQA. |
Jingjing Jiang; Nanning Zheng; | arxiv-cs.CV | 2023-03-02 |
1200 | QAID: Question Answering Inspired Few-shot Intent Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address it, we reformulate intent detection as a question-answering retrieval task by treating utterances and intent names as questions and answers. |
ASAF YEHUDAI et. al. | arxiv-cs.CL | 2023-03-02 |
1201 | A Universal Question-Answering Platform for Knowledge Graphs IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose KGQAn, a universal QA system that does not need to be tailored to each target KG. |
Reham Omar; Ishika Dhall; Panos Kalnis; Essam Mansour; | arxiv-cs.AI | 2023-03-01 |
1202 | RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a retrieval-augmented pretrain-and-finetune paradigm named RAMM for biomedical VQA to overcome the data limitation issue. |
ZHENG YUAN et. al. | arxiv-cs.CV | 2023-03-01 |
1203 | A Question-guided Multi-hop Reasoning Graph Network for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
ZHAOYANG XU et. al. | Inf. Process. Manag. | 2023-03-01 |
1204 | MLPQ: A Dataset for Path Question Answering Over Multilingual Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yiming Tan; Yongrui Chen; G. Qi; Weizhuo Li; Meng Wang; | Big Data Res. | 2023-03-01 |
1205 | Medical Knowledge-based Network for Patient-oriented Visual Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
JIAN HUANG et. al. | Inf. Process. Manag. | 2023-03-01 |
1206 | ERM: Energy-Based Refined-Attention Mechanism for Video Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Spatiotemporal attention learning remains a challenging video question answering (VideoQA) task as it requires a sufficient understanding of cross-modal spatiotemporal … |
Fuwei Zhang; Ruomei Wang; Fan Zhou; Yuanmao Luo; | IEEE Transactions on Circuits and Systems for Video … | 2023-03-01 |
1207 | OEKG: The Open Event Knowledge Graph IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present the Open Event Knowledge Graph (OEKG), a multilingual, event-centric, temporal knowledge graph composed of seven different data sets from multiple application domains, including question answering, entity recommendation and named entity recognition. |
SIMON GOTTSCHALK et. al. | arxiv-cs.AI | 2023-02-28 |
1208 | VQA with Cascade of Self- and Co-Attention Blocks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work aims to learn an improved multi-modal representation through dense interaction of visual and textual modalities. |
Aakansha Mishra; Ashish Anand; Prithwijit Guha; | arxiv-cs.CV | 2023-02-28 |
1209 | Contrastive Video Question Answering Via Video Graph Transformer IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose to perform video question answering (VideoQA) in a Contrastive manner via a Video Graph Transformer model (CoVGT). |
JUNBIN XIAO et. al. | arxiv-cs.CV | 2023-02-27 |
1210 | Cross-Lingual Question Answering Over Knowledge Base As Reading Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel approach for xKBQA in a reading comprehension paradigm. |
CHEN ZHANG et. al. | arxiv-cs.CL | 2023-02-26 |
1211 | Bayesian Networks for Named Entity Prediction in Programming Community Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Within this study, we propose a new approach for natural language processing using Bayesian networks to predict and analyze the context and how this approach can be applied to the Community Question Answering domain. |
Alexey Gorbatovski; Sergey Kovalchuk; | arxiv-cs.LG | 2023-02-26 |
1212 | Choice Fusion As Knowledge for Zero-Shot Dialogue State Tracking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although prior works have leveraged question-answering (QA) data to reduce the need for in-domain training in DST, they fail to explicitly model knowledge transfer and fusion for tracking dialogue states. To address this issue, we propose CoFunDST, which is trained on domain-agnostic QA datasets and directly uses candidate choices of slot-values as knowledge for zero-shot dialogue-state generation, based on a T5 pre-trained language model. |
Ruolin Su; Jingfeng Yang; Ting-Wei Wu; Biing-Hwang Juang; | arxiv-cs.CL | 2023-02-25 |
1213 | Medical Visual Question Answering Using Joint Self-supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study we propose an encoder-decoder framework that leverages the image-text joint representation learned from large-scaled medical image-caption data and adapted to the small-sized medical VQA task. |
Yuan Zhou; Jing Mei; Yiqin Yu; Tanveer Syeda-Mahmood; | arxiv-cs.CV | 2023-02-25 |
1214 | Time-aware Multiway Adaptive Fusion Network for Temporal Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most of existing methods are developed based on pre-trained language models, which might not be capable to learn \emph{temporal-specific} presentations of entities in terms of temporal KGQA task. To alleviate this problem, we propose a novel \textbf{T}ime-aware \textbf{M}ultiway \textbf{A}daptive (\textbf{TMA}) fusion network. |
YONGHAO LIU et. al. | arxiv-cs.CL | 2023-02-24 |
1215 | Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules. |
BAOLIN PENG et. al. | arxiv-cs.CL | 2023-02-24 |
1216 | Extracting Victim Counts from Text Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We cast victim count extraction as a question answering (QA) task with a regression or classification objective. |
Mian Zhong; Shehzaad Dhuliawala; Niklas Stoehr; | arxiv-cs.CL | 2023-02-23 |
1217 | Dr ChatGPT, Tell Me What I Want to Hear: How Prompt Knowledge Impacts Health Answer Correctness IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Generative pre-trained language models (GPLMs) like ChatGPT encode in the model’s parameters knowledge the models observe during the pre-training phase. This knowledge is then … |
G. Zuccon; B. Koopman; | ArXiv | 2023-02-23 |
1218 | EVJVQA Challenge: Multilingual Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this article, we present details of the organization of the challenge, an overview of the methods employed by shared-task participants, and the results. |
Ngan Luu-Thuy Nguyen; Nghia Hieu Nguyen; Duong T. D Vo; Khanh Quoc Tran; Kiet Van Nguyen; | arxiv-cs.CL | 2023-02-22 |
1219 | Question Classification for Thai Conversational Chatbots Using Artificial Neural Networks and Multilingual BERT Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Question-Answering (QA) models are part of Natural Language Processing (NLP) field used for ensuring questions match the answers appropriately. QA consists of several steps, one … |
Kit Thananukhun; S. Jaiyen; Kulsawasd Jitkajornwanich; Anantaporn Hanskunatai; | 2023 15th International Conference on Knowledge and Smart … | 2023-02-21 |
1220 | Interpretable Medical Image Visual Question Answering Via Multi-Modal Relationship Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This technique has the potential to improve the efficiency of medical professionals while relieving the burden on the public health system, particularly in resource-poor countries. Existing medical VQA methods tend to encode medical images and learn the correspondence between visual features and questions without exploiting the spatial, semantic, or medical knowledge behind them. |
XINYUE HU et. al. | arxiv-cs.CV | 2023-02-19 |
1221 | Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a method to measure uncertainty in large language models. |
Lorenz Kuhn; Yarin Gal; Sebastian Farquhar; | arxiv-cs.CL | 2023-02-19 |
1222 | Bridge Damage Cause Estimation Using Multiple Images Based on Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, a bridge member damage cause estimation framework is proposed by calculating the image position using Structure from Motion (SfM) and acquiring its information via Visual Question Answering (VQA). |
Tatsuro Yamane; Pang-jo Chun; Ji Dang; Takayuki Okatani; | arxiv-cs.CV | 2023-02-17 |
1223 | Complex QA and Language Models Hybrid Architectures, Survey IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we start by reviewing required skills and evaluation techniques. |
Xavier Daull; Patrice Bellot; Emmanuel Bruno; Vincent Martin; Elisabeth Murisasco; | arxiv-cs.CL | 2023-02-17 |
1224 | Research on Question Retrieval Method for Community Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yong Sun; Junfang Song; Xiangyu Song; J. Hou; | Multimedia Tools and Applications | 2023-02-17 |
1225 | Bridge The Gap Between Language Models and Tabular Understanding Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Table pretrain-then-finetune paradigm has been proposed and employed at a rapid pace after the success of pre-training in the natural language domain. Despite the promising … |
NUO CHEN et. al. | ArXiv | 2023-02-16 |
1226 | Large-Scale Knowledge Synthesis and Complex Information Retrieval from Biomedical Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our work offers an all-in-one scalable solution for extracting and exploring complex information from large-scale research documents, which would otherwise be tedious. |
SHREYA SAXENA et. al. | arxiv-cs.IR | 2023-02-14 |
1227 | STREET: A Multi-Task Structured Reasoning and Explanation Benchmark IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark. |
DANILO RIBEIRO et. al. | arxiv-cs.CL | 2023-02-13 |
1228 | Analyzing The Effectiveness of The Underlying Reasoning Tasks in Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, it remains an open question as to how effective UR tasks are for the QA task when training models on both tasks in an end-to-end manner. In this study, we address this question by analyzing the effectiveness of UR tasks (including both sentence-level and entity-level tasks) in three aspects: (1) QA performance, (2) reasoning shortcuts, and (3) robustness. |
Xanh Ho; Anh-Khoa Duong Nguyen; Saku Sugawara; Akiko Aizawa; | arxiv-cs.CL | 2023-02-12 |
1229 | Realistic Conversational Question Answering with Answer Selection Based on Calibrated Confidence and Uncertainty Measurement Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Conversational Question Answering (ConvQA) models aim at answering a question with its relevant paragraph and previous question-answer pairs that occurred during conversation multiple times. |
Soyeong Jeong; Jinheon Baek; Sung Ju Hwang; Jong C. Park; | arxiv-cs.CL | 2023-02-10 |
1230 | ControversialQA: Exploring Controversy in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces the first question-answering dataset that defines content controversy by user perception, i.e., votes from plenty of users. |
Zhen Wang; Peide Zhu; Jie Yang; | arxiv-cs.CL | 2023-02-10 |
1231 | Alloprof: A New French Question-answer Education Dataset and Its Use in An Information Retrieval Case Study Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a new public French question-answering dataset collected from Alloprof, a Quebec-based primary and high-school help website, containing 29 349 questions and their explanations in a variety of school subjects from 10 368 students, with more than half of the explanations containing links to other questions or some of the 2 596 reference pages on the website. |
Antoine Lefebvre-Brossard; Stephane Gazaille; Michel C. Desmarais; | arxiv-cs.CL | 2023-02-10 |
1232 | A Biomedical Knowledge Graph for Biomarker Discovery in Cancer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by these, we construct a domain-specific KG, particularly for cancer-specific biomarker discovery. |
Md. Rezaul Karim; Lina Molinas Comet; Oya Beyan; Dietrich Rebholz-Schuhmann; Stefan Decker; | arxiv-cs.AI | 2023-02-09 |
1233 | AI-based Question Answering Assistance for Analyzing Natural-language Requirements Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose QAssist — a question-answering (QA) approach that provides automated assistance to stakeholders, including requirements engineers, during the analysis of NL requirements. |
Saad Ezzini; Sallam Abualhaija; Chetan Arora; Mehrdad Sabetzadeh; | arxiv-cs.SE | 2023-02-09 |
1234 | Robust Question Answering Against Distribution Shifts with Test-Time Adaptation: An Empirical Study Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Robustness tuning (RT) methods have been widely studied to enhance model robustness against distribution shifts before model deployment. |
Hai Ye; Yuyang Ding; Juntao Li; Hwee Tou Ng; | arxiv-cs.CL | 2023-02-09 |
1235 | Robust Question Answering Against Distribution Shifts with Test-Time Adaption: An Empirical Study Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: A deployed question answering (QA) model can easily fail when the test data has a distribution shift compared to the training data. Robustness tuning (RT) methods have been widely … |
Hai Ye; Yuyang Ding; Juntao Li; H. Ng; | Conference on Empirical Methods in Natural Language … | 2023-02-09 |
1236 | ChatGPT Versus Traditional Question Answering for Knowledge Graphs: Current Status and Future Directions Towards Knowledge Graph Chatbots IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Conversational AI and Question-Answering systems (QASs) for knowledge graphs (KGs) are both emerging research areas: they empower users with natural language interfaces for … |
Reham Omar; Omij Mangukiya; Panos Kalnis; Essam Mansour; | ArXiv | 2023-02-08 |
1237 | Analyzing Geographic Questions Using Embedding-based Topic Modeling Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Recently, open-domain question-answering systems have achieved tremendous progress because of developments in large language models (LLMs), and have successfully been applied to … |
Jonghyeon Yang; Hanme Jang; K. Yu; | ISPRS Int. J. Geo Inf. | 2023-02-06 |
1238 | IdT5: Indonesian Version of Multilingual T5 Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, the mT5 model was adapted for only one language, Indonesian, resulting in a pre-trained T5 model that was specific only for Indonesian with a smaller size. |
Mukhlish Fuadi; Adhi Dharma Wibawa; Surya Sumpeno; | arxiv-cs.CL | 2023-02-01 |
1239 | Not All Tasks Are Born Equal: Understanding Zero-Shot Generalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent work has achieved remarkable zero-shot performance with multi-task prompted pretraining, but little has been understood. For the first time, we show that training on a small number of key tasks beats using all the training tasks, while removing these key tasks substantially hurts performance. |
Jing Zhou; Zongyu Lin; Yanan Zheng; Jian Li; Zhilin Yang; | iclr | 2023-02-01 |
1240 | Dual Attention and Question Categorization-Based Visual Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Visual question answering (VQA) aims at predicting an answer to a natural language question associated with an image. This work focuses on two important issues pertaining to VQA, … |
Aakansha Mishra; A. Anand; P. Guha; | IEEE Transactions on Artificial Intelligence | 2023-02-01 |
1241 | Dynamic Dual Graph Networks for Textbook Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
YAXIAN WANG et. al. | Pattern Recognit. | 2023-02-01 |
1242 | UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning. |
Jinhao Jiang; Kun Zhou; Xin Zhao; Ji-Rong Wen; | iclr | 2023-02-01 |
1243 | Leveraging Large Language Models for Multiple Choice Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We show that a model with high MCSB ability performs much better with the natural approach than with the traditional approach across 20 diverse tasks and largely closes the gap with the SOTA, suggesting that the MCQA ability of LLMs has been previously underestimated. |
Joshua Robinson; David Wingate; | iclr | 2023-02-01 |
1244 | STREET: A MULTI-TASK STRUCTURED REASONING AND EXPLANATION BENCHMARK IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark. |
DANILO NEVES RIBEIRO et. al. | iclr | 2023-02-01 |
1245 | DecAF: Joint Decoding of Answers and Logical Forms for Question Answering Over Knowledge Bases IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel KBQA framework that jointly generates both direct answers and logical forms, and then combines them to obtain the final answers. |
DONGHAN YU et. al. | iclr | 2023-02-01 |
1246 | Exploratory Inference Chain: Exploratorily Chaining Multi-hop Inferences with Large Language Models for Question-Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Successful few-shot question-answering with large language models (LLMs) has been reported for a variety of tasks. In the usual approach, an answer is generated by a single call … |
Shosuke Haji; Keiichi Suekane; Hirofumi Sano; T. Takagi; | 2023 IEEE 17th International Conference on Semantic … | 2023-02-01 |
1247 | WikiWhy: Answering and Explaining Cause-and-Effect Questions IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce WikiWhy, a QA dataset built around a novel auxiliary task: explaining why an answer is true in natural language. |
MATTHEW HO et. al. | iclr | 2023-02-01 |
1248 | Generate Rather Than Retrieve: Large Language Models Are Strong Context Generators IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a novel perspective for solving knowledge-intensive tasks by replacing document retrievers with large language model generators. |
WENHAO YU et. al. | iclr | 2023-02-01 |
1249 | Ask Me Anything: A Simple Strategy for Prompting Language Models IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a prompting strategy based on aggregating the predictions of multiple prompts, which enables a 6B parameter model to exceed the few-shot performance of GPT3-175B on 15/20 popular benchmarks. |
SIMRAN ARORA et. al. | iclr | 2023-02-01 |
1250 | Scenario-based Question Answering with Interacting Contextual Properties Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although understanding the relationship between conditions is crucial for solving this challenging QA task, limited work has been done so far in modeling this. In this paper, we propose the T-Reasoner model, which solves this problem with three jointly learned modules: an entailment module which checks whether a condition has been satisfied by the scenario, a decoding module which locates eligible answers from documents, and a reasoning module which infers the relationship between conditions and performs a reasoning step to determine the logically consistent answers and identify missing conditions. |
Haitian Sun; William W. Cohen; Ruslan Salakhutdinov; | iclr | 2023-02-01 |
1251 | Can An AI Win Ghana’s National Science and Maths Quiz? An AI Grand Challenge for Education Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The NSMQ is a Jeopardy-style annual live quiz competition in which 3 teams of 2 students compete by answering questions across biology, chemistry, physics, and math in 5 rounds over 5 progressive stages until a winning team is crowned for that year. In this position paper, we propose the NSMQ AI Grand Challenge, an AI Grand Challenge for Education using Ghana’s National Science and Maths Quiz competition (NSMQ) as a case study. |
George Boateng; Victor Kumbol; Elsie Effah Kaufmann; | arxiv-cs.CL | 2023-01-30 |
1252 | Faithful Chain-of-Thought Reasoning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose Faithful CoT, a reasoning framework involving two stages: Translation (Natural Language query $\rightarrow$ symbolic reasoning chain) and Problem Solving (reasoning chain $\rightarrow$ answer), using an LM and a deterministic solver respectively. |
QING LYU et. al. | arxiv-cs.CL | 2023-01-30 |
1253 | Multi-stage Transfer Learning with BERTology-based Language Models for Question Answering System in Vietnamese Related Papers Related Patents Related Grants Related Venues Related Experts View |
Kiet Van Nguyen; Phong Nguyen-Thuan Do; N. D. Nguyen; A. Nguyen; N. Nguyen; | International Journal of Machine Learning and Cybernetics | 2023-01-30 |
1254 | MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce an alternative scheme based on standard information-theoretic measures in which the information present in the source and summary is directly compared. |
Potsawee Manakul; Adian Liusie; Mark J. F. Gales; | arxiv-cs.CL | 2023-01-28 |
1255 | Graph Attention with Hierarchies for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the well-established HotpotQA benchmark dataset, which requires models to perform answer span extraction as well as support sentence prediction. |
Yunjie He; Philip John Gorinski; Ieva Staliunaite; Pontus Stenetorp; | arxiv-cs.CL | 2023-01-27 |
1256 | Towards A Unified Model for Generating Answers and Explanations in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current systems mostly rely on separate models to predict answers and generate explanations, leading to less grounded and frequently inconsistent results. To address this, we propose a multitask learning approach towards a Unified Model for Answer and Explanation generation (UMAE). |
Chenxi Whitehouse; Tillman Weyde; Pranava Madhyastha; | arxiv-cs.CL | 2023-01-25 |
1257 | ViDeBERTa: A Powerful Pre-trained Language Model for Vietnamese Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents ViDeBERTa, a new pre-trained monolingual language model for Vietnamese, with three versions – ViDeBERTa_xsmall, ViDeBERTa_base, and ViDeBERTa_large, which are pre-trained on a large-scale corpus of high-quality and diverse Vietnamese texts using DeBERTa architecture. |
Cong Dao Tran; Nhut Huy Pham; Anh Nguyen; Truong Son Hy; Tu Vu; | arxiv-cs.CL | 2023-01-25 |
1258 | HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a new dataset, HRVQA, which provides collected 53512 aerial images of 1024*1024 pixels and semi-automatically generated 1070240 QA pairs. |
Kun Li; George Vosselman; Michael Ying Yang; | arxiv-cs.CV | 2023-01-23 |
1259 | PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce PRIMEQA: a one-stop and open-source QA repository with an aim to democratize QA re-search and facilitate easy replication of state-of-the-art (SOTA) QA methods. |
AVIRUP SIL et. al. | arxiv-cs.CL | 2023-01-23 |
1260 | Champion Solution for The WSDM2023 Toloka VQA Challenge Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this report, we present our champion solution to the WSDM2023 Toloka Visual Question Answering (VQA) Challenge. |
Shengyi Gao; Zhe Chen; Guo Chen; Wenhai Wang; Tong Lu; | arxiv-cs.CV | 2023-01-21 |
1261 | Weakly-Supervised Questions for Zero-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, this method required manually creating gold question templates for each new relation. Here, we do away with these gold templates and instead learn a model that can generate questions for unseen relations. |
Saeed Najafi; Alona Fyshe; | arxiv-cs.CL | 2023-01-21 |
1262 | Curriculum Script Distillation for Multilingual Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, since they are limited by the requirements of gold annotated data, most of these advancements do not see the light of day in other languages beyond English. We aim to address this problem by introducing a curriculum based on the source and target language translations to finetune the pre-trained models for the downstream task. |
Khyathi Raghavi Chandu; Alborz Geramifard; | arxiv-cs.CL | 2023-01-17 |
1263 | Multimodal Inverse Cloze Task for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a new pre-training method, Multimodal Inverse Cloze Task, for Knowledge-based Visual Question Answering about named Entities (KVQAE). |
Paul Lerner; Olivier Ferret; Camille Guinaudeau; | arxiv-cs.CL | 2023-01-11 |
1264 | Semantic Web Enabled Geographic Question Answering Framework: GeoTR Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, a question answering framework that converts Turkish natural language input into SPARQL queries in the geographical domain is proposed. |
Ceren Ocal Tasar; Murat Komesli; Murat Osman Unalir; | arxiv-cs.CL | 2023-01-11 |
1265 | There Is No Big Brother or Small Brother:Knowledge Infusion in Language Models for Link Prediction and Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The integration of knowledge graphs with deep learning is thriving in improving the performance of various natural language processing (NLP) tasks. In this paper, we focus on … |
Ankush Agarwal; Sakharam Gawade; Sachin Channabasavarajendra; P. Bhattacharyya; | ICON | 2023-01-10 |
1266 | Review of Artificial Intelligence‐based Question‐answering Systems in Healthcare IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Use of conversational agents, like chatbots, avatars, and robots is increasing worldwide. Yet, their effectiveness in health care is largely unknown. The aim of this advanced … |
Leona Cilar Budler; Lucija Gosak; G. Štiglic; | Wiley Interdisciplinary Reviews: Data Mining and Knowledge … | 2023-01-10 |
1267 | There Is No Big Brother or Small Brother: Knowledge Infusion in Language Models for Link Prediction and Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we focus on knowledge-infused link prediction and question answering using language models, T5, and BLOOM across three domains: Aviation, Movie, and Web. |
Ankush Agarwal; Sakharam Gawade; Sachin Channabasavarajendra; Pushpak Bhattacharyya; | arxiv-cs.CL | 2023-01-10 |
1268 | MAQA: A Multimodal QA Benchmark for Negation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we present a new multimodal question answering (QA) benchmark adapted from labeled music videos in AudioSet (Gemmeke et al., 2017) with the goal of systematically evaluating if multimodal transformers can perform complex reasoning to recognize new concepts as negation of previously learned concepts. |
JUDITH YUE LI et. al. | arxiv-cs.CL | 2023-01-09 |
1269 | RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using The BERT Model Boosted By An Improved ABC Algorithm IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The present paper proposes a method called RLAS-BIABC for AS, which is established on attention mechanism-based long short-term memory (LSTM) and the bidirectional encoder representations from transformers (BERT) word embedding, enriched by an improved artificial bee colony (ABC) algorithm for pretraining and a reinforcement learning-based algorithm for training backpropagation (BP) algorithm. |
Hamid Gharagozlou; Javad Mohammadzadeh; Azam Bastanfard; Saeed Shiry Ghidary; | arxiv-cs.CL | 2023-01-07 |
1270 | A Brain-inspired Memory Transformation Based Differentiable Neural Computer for Reasoning-based Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the learning and memory mechanism of the brain, this paper proposed a Memory Transformation based Differentiable Neural Computer (MT-DNC) model. |
Yao Liang; Hongjian Fang; Yi Zeng; Feifei Zhao; | arxiv-cs.AI | 2023-01-07 |
1271 | Emotion-Cause Pair Extraction As Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we cast the ECPE task to the question answering (QA) problem and propose simple yet effective BERT-based solutions to tackle it. |
Huu-Hiep Nguyen; Minh-Tien Nguyen; | arxiv-cs.CL | 2023-01-05 |
1272 | PIE-QG: Paraphrased Information Extraction for Unsupervised Question Generation from Small Corpora Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Experimenting on five extractive QA datasets demonstrates that our technique achieves on-par performance with existing state-of-the-art QA systems with the benefit of being trained on an order of magnitude fewer documents and without any recourse to external reference data sources. |
Dinesh Nagumothu; Bahadorreza Ofoghi; Guangyan Huang; Peter W. Eklund; | arxiv-cs.CL | 2023-01-03 |
1273 | ChaTA: Towards An Intelligent Question-Answer Teaching Assistant Using Open-Source LLMs Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: To address the challenges of scalable and intelligent question-answering (QA), we introduce an innovative solution that leverages open-source Large Language Models (LLMs) to … |
Yann Hicke; Anmol Agarwal; Qianou Ma; Paul Denny; | ArXiv | 2023-01-01 |
1274 | DUBLIN – Document Understanding By Language-Image Network Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Visual document understanding is a complex task that involves analyzing both the text and the visual elements in document images. Existing models often rely on manual feature … |
KRITI AGGARWAL et. al. | ArXiv | 2023-01-01 |
1275 | Neural Ranking with Weak Supervision for Open-Domain Question Answering : A Survey Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Neural ranking (NR) has become a key component for open-domain question-answering in order to access external knowledge. However, training a good NR model requires substantial … |
XIAOYU SHEN et. al. | Findings | 2023-01-01 |
1276 | Textual Pre-Trained Models for Gender Identification Across Community Question-Answering Members Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Promoting engagement and participation is vital for online social networks such as community Question-Answering (cQA) sites. One way of increasing the contribution of their … |
Pablo Schwarzenberg; A. Figueroa; | IEEE Access | 2023-01-01 |
1277 | Learning Visual Question Answering on Controlled Semantic Noisy Labels IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
HAONAN ZHANG et. al. | Pattern Recognit. | 2023-01-01 |
1278 | MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering Over Tabular and Textual Data Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Transformer-based models for question answering (QA) over tables and texts confront a “long” hybrid sequence over tabular and textual elements, causing long-range reasoning … |
SUNG-MIN LEE et. al. | Findings | 2023-01-01 |
1279 | Retrieval Augmented Generation with Rich Answer Encoding Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Knowledge-intensive generation tasks like generative question answering require models to retrieve appropriate passages from external knowledge sources to support answer … |
Wenyu Huang; Mirella Lapata; P. Vougiouklis; Nikos Papasarantopoulos; Jeff Z. Pan; | International Joint Conference on Natural Language … | 2023-01-01 |
1280 | Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities IF:5 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We introduce the Qwen-VL series, a set of large-scale vision-language models designed to perceive and understand both text and images. Comprising Qwen-VL and Qwen-VL-Chat, these … |
JINZE BAI et. al. | ArXiv | 2023-01-01 |
1281 | A Legal Multi-Choice Question Answering Model Based on BERT and Attention Related Papers Related Patents Related Grants Related Venues Related Experts View |
Guibin Chen; Xudong Luo; Junlin Zhu; | Knowledge Science, Engineering and Management | 2023-01-01 |
1282 | Evaluation of ChatGPT As A Question Answering System for Answering Complex Questions IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
YIMING TAN et. al. | ArXiv | 2023-01-01 |
1283 | Developing An Open Domain Arabic Question Answering System Using A Deep Learning Technique Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: A question-answering system (QAS) retrieves a relevant response to user queries. The existing QASs are limited in performance in satisfying the users’ intention. In recent times, … |
Yazeed Alkhurayyif; A. R. W. Sait; | IEEE Access | 2023-01-01 |
1284 | PMC-LLaMA: Further Finetuning LLaMA on Medical Papers IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Large Language Models (LLMs) have showcased remarkable capabilities in natural language understanding in various domains. These models can usually behave well on daily dialog, or … |
Chaoyi Wu; Xiaoman Zhang; Ya Zhang; Yanfeng Wang; Weidi Xie; | ArXiv | 2023-01-01 |
1285 | Slovak Dataset for Multilingual Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: SK-QuAD is the first manually annotated dataset of questions and answers in Slovak. It consists of more than 91k factual questions and answers from various fields. Each question … |
D. Hládek; J. Staš; J. Juhár; Tomás Koctúr; | IEEE Access | 2023-01-01 |
1286 | SAM-VQA: Supervised Attention-Based Visual Question Answering Model for Post-Disaster Damage Assessment on Remote Sensing Imagery IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Each natural disaster leaves a trail of destruction and damage that must be effectively managed to reduce its negative impact on human life. Any delay in making proper decisions … |
Argho Sarkar; Tashnim Chowdhury; Robin R. Murphy; A. Gangopadhyay; M. Rahnemoonfar; | IEEE Transactions on Geoscience and Remote Sensing | 2023-01-01 |
1287 | OpenRT: An Open-source Framework for Reasoning Over Tabular Data Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: There are a growing number of table pre-training methods proposed for reasoning over tabular data (e.g., question answering, fact checking, and faithful text generation). However, … |
YILUN ZHAO et. al. | Annual Meeting of the Association for Computational … | 2023-01-01 |
1288 | Text-Guided Object Detector for Multi-modal Video Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Video Question Answering (Video QA) is a task to answer a text-format question based on the understanding of linguistic semantics, visual information, and also linguistic-visual … |
Ruoyue Shen; Nakamasa Inoue; K. Shinoda; | 2023 IEEE/CVF Winter Conference on Applications of Computer … | 2023-01-01 |
1289 | Self-Modularized Transformer: Learn to Modularize Networks for Systematic Generalization Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: : Visual Question Answering (VQA) is a task of answering questions about images that fundamentally requires systematic generalization capabilities, i.e., handling novel … |
Y. Kamata; Moyuru Yamada; Takayuki Okatani; | VISIGRAPP | 2023-01-01 |
1290 | Mapping The Challenges of HCI: An Application and Evaluation of ChatGPT and GPT-4 for Cost-Efficient Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Large language models (LLMs), such as ChatGPT and GPT-4, are gaining wide-spread real world use. Yet, the two LLMs are closed source, and little is known about the LLMs’ … |
J. Oppenlaender; J. Hämäläinen; | ArXiv | 2023-01-01 |
1291 | A System for Answering Simple Questions in Multiple Languages Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Our research focuses on the most prevalent type of queries— simple questions —exemplified by questions like “What is the capital of France?”. These questions reference an entity … |
Anton Razzhigaev; Mikhail Salnikov; Valentin Malykh; Pavel Braslavski; A. Panchenko; | Annual Meeting of the Association for Computational … | 2023-01-01 |
1292 | Search-in-the-Chain: Towards The Accurate, Credible and Traceable Content Generation for Complex Knowledge-intensive Tasks IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: With the wide application of Large Language Models (LLMs) such as ChatGPT, how to make the contents generated by LLM accurate and credible becomes very important, especially in … |
Shicheng Xu; Liang Pang; Huawei Shen; Xueqi Cheng; Tat-seng Chua; | ArXiv | 2023-01-01 |
1293 | Automatic Chart Understanding: A Review Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Automated chart analysis has vast potential to improve the accessibility of charts for a wider audience, e.g., people with visual impairments or other disabilities, by generating … |
Ali Mazraeh Farahani; Peyman Adibi; M. Ehsani; H. Hutter; Alireza Darvishy; | IEEE Access | 2023-01-01 |
1294 | Visual Cropping Improves Zero-Shot Question Answering of Multimodal Large Language Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Multimodal Large Language Models (LLMs) have recently achieved promising zero-shot accuracy on visual question answering (VQA) – a fundamental task affecting various downstream … |
Jiarui Zhang; Mahyar Khayatkhoei; P. Chhikara; Filip Ilievski; | ArXiv | 2023-01-01 |
1295 | Qur’an QA 2023 Shared Task: Overview of Passage Retrieval and Reading Comprehension Tasks Over The Holy Qur’an Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Motivated by the need for intelligent question answering (QA) systems on the Holy Qur’an and the success of the first Qur’an Question Answering shared task (Qur’an QA 2022 at … |
Rana Malhas; Watheq Mansour; Tamer Elsayed; | ARABICNLP | 2023-01-01 |
1296 | A Spatial Hierarchical Reasoning Network for Remote Sensing Visual Question Answering IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: For visual question answering on remote sensing (RSVQA), current methods scarcely consider geospatial objects typically with large-scale differences and positional sensitive … |
ZIXIAO ZHANG et. al. | IEEE Transactions on Geoscience and Remote Sensing | 2023-01-01 |
1297 | LowResContextQA at Qur’an QA 2023 Shared Task: Temporal and Sequential Representation Augmented Question Answering Span Detection in Arabic Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The Qur’an holds immense theological and historical significance, and developing a technology-driven solution for answering questions from this sacred text is of paramount … |
Hariram Veeramani; Surendrabikram Thapa; Usman Naseem; | ARABICNLP | 2023-01-01 |
1298 | Benchmarking Geospatial Question Answering Engines Using The Dataset GeoQuestions1089 Related Papers Related Patents Related Grants Related Venues Related Experts View |
SERGIOS-ANESTIS KEFALIDIS et. al. | International Workshop on the Semantic Web | 2023-01-01 |
1299 | LogiQA 2.0—An Improved Dataset for Logical Reasoning in Natural Language Understanding IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: NLP research on logical reasoning regains momentum with the recent releases of a handful of datasets, notably LogiQA and Reclor. Logical reasoning is exploited in many probing … |
HANMENG LIU et. al. | IEEE/ACM Transactions on Audio, Speech, and Language … | 2023-01-01 |
1300 | Structure-Discourse Hierarchical Graph for Conditional Question Answering on Long Documents Related Papers Related Patents Related Grants Related Venues Related Experts View |
HAOWEI DU et. al. | Annual Meeting of the Association for Computational … | 2023-01-01 |
1301 | Overcoming Language Priors Via Shuffling Language Bias for Robust Visual Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Recent research has revealed the notorious language prior problem in visual question answering (VQA) tasks based on visual-textual interaction, which indicates that well-developed … |
Jia Zhao; Z. Yu; X. Zhang; Ying Yang; | IEEE Access | 2023-01-01 |
1302 | Think-on-Graph: Deep and Responsible Reasoning of Large Language Model with Knowledge Graph IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Large language models (LLMs) have made significant strides in various tasks, yet they often struggle with complex reasoning and exhibit poor performance in scenarios where … |
JIASHUO SUN et. al. | ArXiv | 2023-01-01 |
1303 | Billie-Newman at SemEval-2023 Task 5: Clickbait Classification and Question Answering with Pre-Trained Language Models, Named Entity Recognition and Rule-Based Approaches Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In this paper, we describe the implementations of our systems for the SemEval-2023 Task 5 ‘Clickbait Spoiling’, which involves the classification of clickbait posts in sub-task 1 … |
Andreas Kruff; Anh Huy Matthias Tran; | International Workshop on Semantic Evaluation | 2023-01-01 |
1304 | Generator-Retriever-Generator: A Novel Approach to Open-domain Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Open-domain question answering (QA) tasks usually require the retrieval of relevant information from a large corpus to generate accurate answers. We propose a novel approach … |
Abdelrahman Abdallah; Adam Jatowt; | ArXiv | 2023-01-01 |
1305 | BERT Shows Garden Path Effects Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Garden path sentences (i.e. “the horse raced past the barn fell”) are sentences that readers initially incorrectly parse, requiring partial or total re-analysis of the sentence … |
Tovah Irwin; Kyra Wilson; A. Marantz; | Conference of the European Chapter of the Association for … | 2023-01-01 |
1306 | Knowledge Graph Enabled Open-Domain Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Joel Oduro-Afriyie; Hasan M. Jamil; | International Conference on Flexible Query Answering Systems | 2023-01-01 |
1307 | RecallM: An Architecture for Temporal Context Understanding and Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: —The ideal long-term memory mechanism for Large Language Model (LLM) based chatbots, would lay the foundation for continual learning, complex reasoning and allow sequential and … |
Brandon Kynoch; Hugo Latapie; | ArXiv | 2023-01-01 |
1308 | DeepMaven: Deep Question Answering on Long-Distance Movie/TV Show Videos with Multimedia Knowledge Extraction and Synthesis Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Long video content understanding poses a challenging set of research questions as it involves long-distance, cross-media reasoning and knowledge awareness. In this paper, we … |
Y. FUNG et. al. | Conference of the European Chapter of the Association for … | 2023-01-01 |
1309 | Hitachi at SemEval-2023 Task 4: Exploring Various Task Formulations Reveals The Importance of Description Texts on Human Values Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This paper describes our participation in SemEval-2023 Task 4, ValueEval: Identification of Human Values behind Arguments. The aim of this task is to identify whether or not an … |
Masaya Tsunokake; Atsuki Yamaguchi; Yuta Koreeda; Hiroaki Ozaki; Yasuhiro Sogawa; | International Workshop on Semantic Evaluation | 2023-01-01 |
1310 | Why Does ChatGPT Fall Short in Answering Questions Faithfully? IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Recent advancements in Large Language Models, such as ChatGPT, have demonstrated significant potential to impact various aspects of human life. However, ChatGPT still faces … |
Shen Zheng; Jie Huang; K. Chang; | ArXiv | 2023-01-01 |
1311 | CALM-Bench: A Multi-task Benchmark for Evaluating Causality-Aware Language Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Causal reasoning is a critical component of human cognition and is required across a range of question-answering (QA) tasks (such as abductive reasoning, commonsense QA, and … |
Dhairya Dalal; P. Buitelaar; Mihael Arcan; | Findings | 2023-01-01 |
1312 | Hybrid Hierarchical Retrieval for Open-Domain Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Retrieval accuracy is crucial to the performance of open-domain question answering (ODQA) systems. Recent work has demonstrated that dense hierarchical retrieval (DHR), which … |
MANOJ GHUHAN ARIVAZHAGAN et. al. | Annual Meeting of the Association for Computational … | 2023-01-01 |
1313 | Towards Improving The Reliability and Transparency of ChatGPT for Educational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yongchao Wu; Aron Henriksson; Martin Duneld; Jalal Nouri; | European Conference on Technology Enhanced Learning | 2023-01-01 |
1314 | Improved Blending Attention Mechanism in Visual Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Visual question answering (VQA) has attracted more and more attention in computer vision and natural language processing. Scholars are committed to studying how to better … |
SIYU LU et. al. | Comput. Syst. Sci. Eng. | 2023-01-01 |
1315 | How to Practice VQA on A Resource-limited Target Domain Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Visual question answering (VQA) is an active research area at the intersection of computer vision and natural language understanding. One major obstacle that keeps VQA models that … |
Mingda Zhang; R. Hwa; Adriana Kovashka; | 2023 IEEE/CVF Winter Conference on Applications of Computer … | 2023-01-01 |
1316 | Overcoming Language Priors with Counterfactual Inference for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
ZHIBO REN et. al. | China National Conference on Chinese Computational … | 2023-01-01 |
1317 | Gradually Excavating External Knowledge for Implicit Complex Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
CHANG LIU et. al. | Conference on Empirical Methods in Natural Language … | 2023-01-01 |
1318 | Language Models As Controlled Natural Language Semantic Parsers for Knowledge Graph Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: . We propose the use of controlled natural language as a target for knowledge graph question answering (KGQA) semantic parsing via language models as opposed to using formal query … |
Jens Lehmann; Sébastien Ferré; S. Vahdati; | European Conference on Artificial Intelligence | 2023-01-01 |
1319 | PubMedCLIP: How Much Does CLIP Benefit Visual Question Answering in The Medical Domain? IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Contrastive Language–Image Pre-training (CLIP) has shown remarkable success in learning with cross-modal supervision from extensive amounts of image–text pairs collected online. … |
Sedigheh Eslami; C. Meinel; Gerard de Melo; | Findings | 2023-01-01 |
1320 | Multistep Question-Driven Visual Question Answering for Remote Sensing Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Visual question answering (VQA) aims to build an interactive system that infers the answer according to the input image and text-based question. Recently, VQA for remote sensing … |
Mei-mei Zhang; Fang Chen; Bin Li; | IEEE Transactions on Geoscience and Remote Sensing | 2023-01-01 |
1321 | DRLK: Dynamic Hierarchical Reasoning with Language Model and Knowledge Graph for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose DRLK (Dynamic Hierarchical Reasoning with Language Model and Knowledge Graphs), a novel model that utilizes dynamic hierarchical interactions between the QA context and KG for reasoning. |
Miao Zhang; Rufeng Dai; Ming Dong; Tingting He; | emnlp | 2022-12-30 |
1322 | OpenCQA: Open-ended Question Answering with Charts IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Answering such questions are often difficult and time-consuming as it requires a lot of cognitive and perceptual efforts. To address this challenge, we introduce a new task called OpenCQA, where the goal is to answer an open-ended question about a chart with descriptive texts. |
SHANKAR KANTHARAJ et. al. | emnlp | 2022-12-30 |
1323 | A Sequential Flow Control Framework for Multi-hop Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing methods, however, (i) infer the dynamic question representation only through coarse-grained attention mechanisms, which may bring information loss, (ii) and have not effectively modeled the sequential logic, which is crucial for the multi-hop reasoning process in KBQA. To address these issues, we propose a sequential reasoning self-attention mechanism to capture the crucial reasoning information of each single hop in a more fine-grained way. |
Minghui Xie; Chuzhan Hao; Peng Zhang; | emnlp | 2022-12-30 |
1324 | Successive Prompting for Decomposing Complex Questions IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a way to generate synthetic dataset which can be used to bootstrap model?s ability to decompose and answer intermediate questions. |
Dheeru Dua; Shivanshu Gupta; Sameer Singh; Matt Gardner; | emnlp | 2022-12-30 |
1325 | Two Is Better Than Many? Binary Classification As An Effective Approach to Multi-Choice Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a simple refactoring of multi-choice question answering (MCQA) tasks as a series of binary classifications. |
Deepanway Ghosal; Navonil Majumder; Rada Mihalcea; Soujanya Poria; | emnlp | 2022-12-30 |
1326 | Towards Teachable Reasoning Systems: Using A Dynamic Memory of User Feedback for Continual System Improvement IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our goal is a teachable reasoning system for question-answering (QA), where a user can interact with faithful answer explanations, and correct its errors so that the system improves over time. |
Bhavana Dalvi Mishra; Oyvind Tafjord; Peter Clark; | emnlp | 2022-12-30 |
1327 | SimQA: Detecting Simultaneous MT Errors Through Word-by-Word Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Yet, evaluations of simultaneous machine translation (SimulMT) fail to capture if systems correctly translate the most salient elements of a question: people, places, and dates. To address this problem, we introduce a downstream word-by-word question answering evaluation task (SimQA): given a source language question, translate the question word by word into the target language, and answer as soon as possible. |
HyoJung Han; Marine Carpuat; Jordan Boyd-Graber; | emnlp | 2022-12-30 |
1328 | Multi-VQG: Generating Engaging Questions for Multiple Images Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose generating engaging questions from multiple images. |
Min-Hsuan Yeh; Vincent Chen; Ting-Hao Huang; Lun-Wei Ku; | emnlp | 2022-12-30 |
1329 | InforMask: Unsupervised Informative Masking for Language Model Pretraining Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose InforMask, a new unsupervised masking strategy for training masked language models. |
Nafis Sadeq; Canwen Xu; Julian McAuley; | emnlp | 2022-12-30 |
1330 | TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions Over Tabular Data IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, auto-regressive PLMs are challenged by recent emerging numerical reasoning datasets, such as TAT-QA, due to the error-prone implicit calculation. In this paper, we present TaCube, to pre-compute aggregation/arithmetic results for the table in advance, so that they are handy and readily available for PLMs to answer numerical reasoning questions. |
FAN ZHOU et. al. | emnlp | 2022-12-30 |
1331 | ConvFinQA: Exploring The Chain of Numerical Reasoning in Conversational Finance Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we investigate the application domain of finance that involves real-world, complex numerical reasoning. |
ZHIYU CHEN et. al. | emnlp | 2022-12-30 |
1332 | QA Domain Adaptation Using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel self-supervised framework called QADA for QA domain adaptation. |
Zhenrui Yue; Huimin Zeng; Bernhard Kratzwald; Stefan Feuerriegel; Dong Wang; | emnlp | 2022-12-30 |
1333 | Deep Learning-based Question Answering: A Survey IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Heba Abdelnabi; A. Awajan; Mostafa Z. Ali; | Knowledge and Information Systems | 2022-12-30 |
1334 | CycleKQR: Unsupervised Bidirectional Keyword-Question Rewriting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the keyword-question rewriting task to improve query understanding capabilities of NLU systems for all surface forms. To achieve this, we present CycleKQR, an unsupervised approach, enabling effective rewriting between keyword and question queries using non-parallel data. |
ANDREA IOVINE et. al. | emnlp | 2022-12-30 |
1335 | CRIPP-VQA: Counterfactual Reasoning About Implicit Physical Properties Via Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce CRIPP-VQA, a new video question answering dataset for reasoning about the implicit physical properties of objects in a scene. |
Maitreya Patel; Tejas Gokhale; Chitta Baral; Yezhou Yang; | emnlp | 2022-12-30 |
1336 | Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our goal is a question-answering (QA) system that can show how its answers are implied by its own internal beliefs via a systematic chain of reasoning. |
Oyvind Tafjord; Bhavana Dalvi Mishra; Peter Clark; | emnlp | 2022-12-30 |
1337 | Explainable Question Answering Based on Semantic Graph By Global Differentiable Learning and Dynamic Adaptive Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To alleviate it, we propose a simple yet effective Global Differentiable Learning strategy to explore optimal reasoning paths from the latent probability space so that the model learns to solve intermediate reasoning processes without expert annotations. |
JIANGUO MAO et. al. | emnlp | 2022-12-30 |
1338 | Knowledge Transfer from Answer Ranking to Answer Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to train a GenQA model by transferring knowledge from a trained AS2 model, to overcome the aforementioned issue. |
Matteo Gabburo; Rik Koncel-Kedziorski; Siddhant Garg; Luca Soldaini; Alessandro Moschitti; | emnlp | 2022-12-30 |
1339 | Retrieval-Augmented Generative Question Answering for Event Argument Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a retrieval-augmented generative QA model (R-GQA) for event argument extraction. |
Xinya Du; Heng Ji; | emnlp | 2022-12-30 |
1340 | Rethinking Multi-Modal Alignment in Multi-Choice VideoQA from Feature and Sample Perspectives Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we reconsider the multi-modal alignment problem in VideoQA from feature and sample perspectives to achieve better performance. |
SHAONING XIAO et. al. | emnlp | 2022-12-30 |
1341 | Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to conduct numerical capability diagnosis on a series of Numerical Question Answering systems and datasets. |
Jialiang Xu; Mengyu Zhou; Xinyi He; Shi Han; Dongmei Zhang; | emnlp | 2022-12-30 |
1342 | Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present the first systematic conceptual and data-driven analysis to examine the shortcomings of token-level equivalence measures. |
Jannis Bulian; Christian Buck; Wojciech Gajewski; Benjamin B�rschinger; Tal Schuster; | emnlp | 2022-12-30 |
1343 | Video Question Answering: Datasets, Algorithms and Challenges IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This survey aims to sort out the recent advances in video question answering (VideoQA) and point towards future directions. |
YAOYAO ZHONG et. al. | emnlp | 2022-12-30 |
1344 | Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Also, the naturally available supervision (whether the passage contains the correct answer) is weak and does not guarantee question relevancy. To address these issues, we propose an Entity-Focused Retrieval (EnFoRe) model that provides stronger supervision during training and recognizes question-relevant entities to help retrieve more specific knowledge. |
Jialin Wu; Raymond Mooney; | emnlp | 2022-12-30 |
1345 | Graph-Induced Transformers for Efficient Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our work proposes the Graph-Induced Transformer (GIT) that applies graph-derived attention patterns directly into a PLM, without the need to employ external graph modules. |
Giwon Hong; Jeonghwan Kim; Junmo Kang; Sung-Hyon Myaeng; | emnlp | 2022-12-30 |
1346 | Teaching Broad Reasoning Skills for Multi-Step QA By Generating Hard Contexts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We show how to use question decompositions to teach language models these broad reasoning skills in a robust fashion. |
Harsh Trivedi; Niranjan Balasubramanian; Tushar Khot; Ashish Sabharwal; | emnlp | 2022-12-30 |
1347 | Empowering Language Models with Knowledge Graph Reasoning for Open-Domain Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose knOwledge REasOning empowered Language Model(OREO-LM), which consists of a novel Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. |
ZINIU HU et. al. | emnlp | 2022-12-30 |
1348 | Improving Compositional Generalization for Multi-step Quantitative Reasoning in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Quantitative reasoning is an important aspect of question answering, especially when numeric and verbal cues interact to indicate sophisticated, multi-step programs. In this paper, we demonstrate how modeling the compositional nature of quantitative text can enhance the performance and robustness of QA models, allowing them to capture arithmetic logic that is expressed verbally. |
Armineh Nourbakhsh; Cathy Jiao; Sameena Shah; Carolyn Ros�; | emnlp | 2022-12-30 |
1349 | Towards A Unified Multi-Dimensional Evaluator for Text Generation IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a unified multi-dimensional evaluator UniEval for NLG. |
MING ZHONG et. al. | emnlp | 2022-12-30 |
1350 | Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose Uni-Parser, a unified semantic parser for question answering (QA) on both KB and DB. |
YE LIU et. al. | emnlp | 2022-12-30 |
1351 | Varifocal Question Generation for Fact-checking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present Varifocal, a method that generates questions based on different focal points within a given claim, i.e. different spans of the claim and its metadata, such as its source and date. |
Nedjma Ousidhoum; Zhangdie Yuan; Andreas Vlachos; | emnlp | 2022-12-30 |
1352 | Few-shot Query-Focused Summarization with Prefix-Merging Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the idea that whether we can integrate and transfer the knowledge of text summarization and question answering to assist the few-shot learning in query-focused summarization. |
Ruifeng Yuan; Zili Wang; Ziqiang Cao; Wenjie Li; | emnlp | 2022-12-30 |
1353 | Improving Passage Retrieval with Zero-Shot Question Generation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a simple and effective re-ranking method for improving passage retrieval in open question answering. |
DEVENDRA SACHAN et. al. | emnlp | 2022-12-30 |
1354 | Retrieval Augmented Visual Question Answering with Outside Knowledge IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Instead, we propose a joint training scheme which includes differentiable DPR integrated with answer generation so that the system can be trained in an end-to-end fashion. |
Weizhe Lin; Bill Byrne; | emnlp | 2022-12-30 |
1355 | Learning to Explain Selectively: A Case Study on Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose learning to explain�selectively�: for each decision that the user makes, we use a model to choose the best explanation from a set of candidates and update this model with feedback to optimize human performance. |
Shi Feng; Jordan Boyd-Graber; | emnlp | 2022-12-30 |
1356 | UniRPG: Unified Discrete Reasoning Over Table and Text As Program Generation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose UniRPG, a semantic-parsing-based approach advanced in interpretability and scalability, to perform Unified discrete Reasoning over heterogeneous knowledge resources, i. e. , table and text, as Program Generation. |
YONGWEI ZHOU et. al. | emnlp | 2022-12-30 |
1357 | Enhancing Self-Consistency and Performance of Pre-Trained Language Models Through Natural Language Inference IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: . To address this failure mode, we propose a framework, Consistency Correction through Relation Detection, or ConCoRD, for boosting the consistency and accuracy of pre-trained NLP models using pre-trained natural language inference (NLI) models without fine-tuning or re-training. |
ERIC MITCHELL et. al. | emnlp | 2022-12-30 |
1358 | Summarizing Community-based Question-Answer Pairs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To help users quickly digest the key information, we propose the novel CQA summarization task that aims to create a concise summary from CQA pairs. |
Ting-Yao Hsu; Yoshi Suhara; Xiaolan Wang; | emnlp | 2022-12-30 |
1359 | Improving Complex Knowledge Base Question Answering Via Question-to-Action and Question-to-Question Alignment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, there is a significant semantic and structural gap between natural language and action sequences, which makes this conversion difficult. In this paper, we introduce an alignment-enhanced complex question answering framework, called ALCQA, which mitigates this gap through question-to-action alignment and question-to-question alignment. |
Yechun Tang; Xiaoxia Cheng; Weiming Lu; | emnlp | 2022-12-30 |
1360 | KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, most existing approaches for MRC may perform poorly in the few-shot learning scenario. To solve this issue, we propose a novel framework named Knowledge Enhanced Contrastive Prompt-tuning (KECP). |
JIANING WANG et. al. | emnlp | 2022-12-30 |
1361 | FiE: Building A Global Probability Space By Leveraging Early Fusion in Encoder for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose to extend transformer encoders with the ability to fuse information from multiple passages, using global representation to provide cross-sample attention over all tokens across samples. |
Akhil Kedia; Mohd Abbas Zaidi; Haejun Lee; | emnlp | 2022-12-30 |
1362 | Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: A key challenge in building and evaluating models for this type of discourse comprehension is the lack of annotated data, especially since collecting answers to such questions requires high cognitive load for annotators. This paper presents a novel paradigm that enables scalable data collection targeting the comprehension of news documents, viewing these questions through the lens of discourse. |
WEI-JEN KO et. al. | emnlp | 2022-12-30 |
1363 | You Only Need One Model for Open-domain Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This allows us to use a single question answering model trained end-to-end, which is a more efficient use of model capacity and also leads to better gradient flow. We present a pre-training method to effectively train this architecture and evaluate our model on the Natural Questions and TriviaQA open datasets. |
HAEJUN LEE et. al. | emnlp | 2022-12-30 |
1364 | Learning to Generate Question By Asking Question: A Primal-Dual Approach with Uncommon Word Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, unseen or rare word generation has not been studied in previous works. In this paper, we propose a novel approach which incorporates question generation with its dual problem, question answering, into a unified primal-dual framework. |
QIFAN WANG et. al. | emnlp | 2022-12-30 |
1365 | Exploring Dual Encoder Architectures for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we explore the dual encoder architectures for QA retrieval tasks. |
ZHE DONG et. al. | emnlp | 2022-12-30 |
1366 | TASA: Deceiving Question Answering Models By Twin Answer Sentences Attack Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present Twin Answer Sentences Attack (TASA), an adversarial attack method for question answering (QA) models that produces fluent and grammatical adversarial contexts while maintaining gold answers. |
YU CAO et. al. | emnlp | 2022-12-30 |
1367 | ScienceWorld: Is Your Agent Smarter Than A 5th Grader? IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present ScienceWorld, a benchmark to test agents� scientific reasoning abilities in a new interactive text environment at the level of a standard elementary school science curriculum. |
Ruoyao Wang; Peter Jansen; Marc-Alexandre C�t�; Prithviraj Ammanabrolu; | emnlp | 2022-12-30 |
1368 | Retrieval As Attention: End-to-end Learning of Retrieval and Reading Within A Single Transformer IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: These two components are usually modeled separately, which necessitates a cumbersome implementation and is awkward to optimize in an end-to-end fashion. In this paper, we revisit this design and eschew the separate architecture and training in favor of a single Transformer that performs retrieval as attention (RAA), and end-to-end training solely based on supervision from the end QA task. |
ZHENGBAO JIANG et. al. | emnlp | 2022-12-30 |
1369 | Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present Rainier, or Reinforced Knowledge Introspector, that learns to generate contextually relevant knowledge in response to given questions. |
JIACHENG LIU et. al. | emnlp | 2022-12-30 |
1370 | DSM: Question Generation Over Knowledge Base Via Modeling Diverse Subgraphs with Meta-learner IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we show that making use of the past experience on semantically similar subgraphs can reduce the learning difficulty and promote the performance of KBQG models. To achieve this, we propose a novel approach to model diverse subgraphs with meta-learner (DSM). |
SHASHA GUO et. al. | emnlp | 2022-12-30 |
1371 | Passage-Mask: A Learnable Regularization Strategy for Retriever-Reader Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Retriever-reader models achieve competitive performance across many different NLP tasks such as open question answering and dialogue conversations. In this work, we notice these models easily overfit the top-rank retrieval passages and standard training fails to reason over the entire retrieval passages. |
Shujian Zhang; Chengyue Gong; Xingchao Liu; | emnlp | 2022-12-30 |
1372 | An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To combine the strength of both approaches, we propose the Efficient Memory-Augmented Transformer (EMAT) – it encodes external knowledge into a key-value memory and exploits the fast maximum inner product search for memory querying. |
YUXIANG WU et. al. | emnlp | 2022-12-30 |
1373 | Capturing Global Structural Information in Long Document Question Answering with Compressive Graph Selector Network Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these methods usually ignore the global structure of the long document, which is essential for long-range understanding. To tackle this problem, we propose Compressive Graph Selector Network (CGSN) to capture the global structure in a compressive and iterative manner. |
Yuxiang Nie; Heyan Huang; Wei Wei; Xian-Ling Mao; | emnlp | 2022-12-30 |
1374 | CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, it can be expensive to re-train well-established retrievers such as search engines that are originally developed for non-conversational queries. To facilitate their use, we develop a query rewriting model CONQRR that rewrites a conversational question in the context into a standalone question. |
ZEQIU WU et. al. | emnlp | 2022-12-30 |
1375 | Leveraging QA Datasets to Improve Generative Data Augmentation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose CONDA, an approach to further improve GLM�s ability to generate synthetic data by reformulating data generation as context generation for a given question-answer (QA) pair and leveraging QA datasets for training context generators. |
Dheeraj Mekala; Tu Vu; Timo Schick; Jingbo Shang; | emnlp | 2022-12-30 |
1376 | Faithful Knowledge Graph Explanations in Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A common way of incorporating facts from the graph is to encode them separately from the question, and then combine the two representations to select an answer. In this paper, we argue that highly faithful graph-based explanations cannot be extracted from existing models of this type. |
Guy Aglionby; Simone Teufel; | emnlp | 2022-12-30 |
1377 | MonoQA: Multi-Task Learning of Reranking and Answer Extraction for Open-Retrieval Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the use of Multi-Task Learning (MTL) to improve performance on the ORConvQA task by sharing the reranker and reader�s learned structure in a generative model. |
Sarawoot Kongyoung; Craig Macdonald; Iadh Ounis; | emnlp | 2022-12-30 |
1378 | A Survey on Table-and-Text HybridQA: Concepts, Methods, Challenges and Future Directions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The contributions of this paper can be summarized in three folds: (1) first survey, to our best knowledge, including benchmarks, methods and challenges for HybridQA; (2) systematic investigation with the reasonable comparison of the existing systems to articulate their advantages and shortcomings; (3) detailed analysis of challenges in four important dimensions to shed light on future directions. |
Dingzirui Wang; Longxu Dou; Wanxiang Che; | arxiv-cs.CL | 2022-12-27 |
1379 | UnICLAM: Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Medical Visual Question Answering (Medical-VQA) aims to to answer clinical questions regarding radiology images, assisting doctors with decision-making options. Nevertheless, … |
Chenlu Zhan; Peng Peng; Hongsen Wang; Tao Chen; Hongwei Wang; | ArXiv | 2022-12-21 |
1380 | Language Models Are Better Than Humans at Next-token Prediction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Current language models are considered to have sub-human capabilities at natural language tasks like question-answering or writing code. However, language models are not trained … |
Buck Shlegeris; Fabien Roger; Lawrence Chan; Euan McLean; | ArXiv | 2022-12-21 |
1381 | UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose UnICLAM, a Unified and Interpretable Medical-VQA model through Contrastive Representation Learning with Adversarial Masking. |
Chenlu Zhan; Peng Peng; Hongsen Wang; Tao Chen; Hongwei Wang; | arxiv-cs.CV | 2022-12-20 |
1382 | (QA)^2: Question Answering with Questionable Assumptions IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Naturally occurring information-seeking questions often contain questionable assumptions—assumptions that are false or unverifiable. Questions containing questionable assumptions … |
Najoung Kim; Phu Mon Htut; Sam Bowman; Jackson Petty; | ArXiv | 2022-12-20 |
1383 | WeCheck: Strong Factual Consistency Checker Via Weakly Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As a result, they perform poorly on the real generated text and are biased heavily by their single-source upstream tasks. To alleviate this problem, we propose a weakly supervised framework that aggregates multiple resources to train a precise and efficient factual metric, namely WeCheck. |
WENHAO WU et. al. | arxiv-cs.CL | 2022-12-20 |
1384 | Analyzing Semantic Faithfulness of Language Models Via Input Intervention on Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we consider three transformer models, BERT, RoBERTa, and XLNet, in both small and large versions, and investigate how faithful their representations are with respect to the semantic content of texts. |
Akshay Chaturvedi; Swarnadeep Bhar; Soumadeep Saha; Utpal Garain; Nicholas Asher; | arxiv-cs.CL | 2022-12-20 |
1385 | Do I Have The Knowledge to Answer? Investigating Answerability of Knowledge Base Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Experimenting with three state-of-the-art KBQA models, we find that all three models suffer a drop in performance even after suitable adaptation for unanswerable questions. |
Mayur Patidar; Prayushi Faldu; Avinash Singh; Lovekesh Vig; Indrajit Bhattacharya; | arxiv-cs.CL | 2022-12-20 |
1386 | (QA)$^2$: Question Answering with Questionable Assumptions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose (QA)$^2$ (Question Answering with Questionable Assumptions), an open-domain evaluation dataset consisting of naturally occurring search engine queries that may or may not contain questionable assumptions. |
Najoung Kim; Phu Mon Htut; Samuel R. Bowman; Jackson Petty; | arxiv-cs.CL | 2022-12-20 |
1387 | Defending Against Disinformation Attacks in Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To find it, we introduce a method that uses query augmentation to search for a diverse set of passages that could answer the original question but are less likely to have been poisoned. |
Orion Weller; Aleem Khan; Nathaniel Weir; Dawn Lawrie; Benjamin Van Durme; | arxiv-cs.CL | 2022-12-20 |
1388 | Rethinking Label Smoothing on Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we analyze the primary factors limiting the performance of multi-hop reasoning and introduce label smoothing into the MHQA task. |
ZHANGYUE YIN et. al. | arxiv-cs.CL | 2022-12-19 |
1389 | Medical Knowledge Graph QA for Drug-Drug Interaction Prediction Based on Multi-hop Machine Reading Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a medical knowledge graph question answering model, dubbed MedKGQA, that predicts drug-drug interaction by employing machine reading comprehension from closed-domain literature and constructing a knowledge graph of drug-protein triplets from open-domain documents. |
Peng Gao; Feng Gao; Jian-Cheng Ni; Yu Wang; Fei Wang; | arxiv-cs.CL | 2022-12-19 |
1390 | Source-Free Domain Adaptation for Question Answering with Masked Self-training Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we investigate a more challenging setting, source-free UDA, in which we have only the pretrained source model and target domain data, without access to source domain data. |
M. Yin; B. Wang; Y. Dong; C. Ling; | arxiv-cs.CL | 2022-12-19 |
1391 | Visconde: Multi-document QA with GPT-3 and Neural Reranking IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a question-answering system that can answer questions whose supporting evidence is spread over multiple (potentially long) documents. |
Jayr Pereira; Robson Fidalgo; Roberto Lotufo; Rodrigo Nogueira; | arxiv-cs.CL | 2022-12-19 |
1392 | Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: For example, in extractive question answering (QA), generative models have constantly yielded state-of-the-art results. In this work, we identify the issue of tokenization inconsistency that is commonly neglected in training these models. |
KAISER SUN et. al. | arxiv-cs.CL | 2022-12-19 |
1393 | Task Preferences Across Languages on Community Question Answering Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, the rapid proliferation and localization of these platforms spanning geographic and linguistic boundaries offer a unique opportunity to study the task requirements and preferences of users in different socio-linguistic groups. In this study, we implement an entity-embedding model trained on a large longitudinal dataset of multi-lingual and task-oriented question-answer pairs to uncover and quantify the (i) prevalence and distribution of various online tasks across linguistic communities, and (ii) emerging and receding trends in task popularity over time in these communities. |
Sebastin Santy; Prasanta Bhattacharya; Rishabh Mehrotra; | arxiv-cs.CL | 2022-12-18 |
1394 | Towards Leveraging Latent Knowledge and Dialogue Context for Real-world Conversational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose to leverage latent knowledge in existing conversation logs via a neural Retrieval-Reading system, enhanced with a TFIDF-based text summarizer refining lengthy conversational history to alleviate the long context issue. |
Shaomu Tan; Denis Paperno; | arxiv-cs.CL | 2022-12-17 |
1395 | Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Medical visual question answering (Med-VQA) is to answer medical questions based on clinical images provided. This field is still in its infancy due to the complexity of the trio … |
ZHONGTIAN SUN et. al. | 2022 IEEE International Conference on Big Data (Big Data) | 2022-12-17 |
1396 | PolQA: Polish Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce and publicly release PolQA, the first Polish dataset for OpenQA. |
Piotr Rybak; Piotr Przybyła; Maciej Ogrodniczuk; | arxiv-cs.CL | 2022-12-17 |
1397 | Enhancing Multi-modal and Multi-hop Question Answering Via Structured Knowledge and Unified Retrieval-Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, the pipelined approaches of retrieval and generation might result in poor generation performance when retrieval performance is low. To address these issues, we propose a Structured Knowledge and Unified Retrieval-Generation (SKURG) approach. |
Qian Yang; Qian Chen; Wen Wang; Baotian Hu; Min Zhang; | arxiv-cs.CL | 2022-12-16 |
1398 | Self-Prompting Large Language Models for Zero-Shot Open-Domain QA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a Self-Prompting framework to explicitly utilize the massive knowledge encoded in the parameters of LLMs and their strong instruction understanding abilities. |
Junlong Li; Jinyuan Wang; Zhuosheng Zhang; Hai Zhao; | arxiv-cs.CL | 2022-12-16 |
1399 | Enhancing Multi-modal Multi-hop Question Answering Via Structured Knowledge and Unified Retrieval-Generation Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Multi-modal multi-hop question answering involves answering a question by reasoning over multiple input sources from different modalities. Existing methods often retrieve … |
Qian Yang; Qian Chen; Wen Wang; Baotian Hu; Min Zhang; | Proceedings of the 31st ACM International Conference on … | 2022-12-16 |
1400 | Self-Prompting Large Language Models for Open-Domain QA IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Open-Domain Question Answering (ODQA) requires models to answer factoid questions with no context given. The common way for this task is to train models on a large-scale annotated … |
Junlong Li; Zhuosheng Zhang; Hai Zhao; | ArXiv | 2022-12-16 |
1401 | Saved You A Click: Automatically Answering Clickbait Titles Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We find that both extractive and abstractive models improve significantly after finetuning. |
Oliver Johnson; Beicheng Lou; Janet Zhong; Andrey Kurenkov; | arxiv-cs.CL | 2022-12-15 |
1402 | Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a reproducible evaluation framework for the task and benchmark a broad set of architectures. |
BERND BOHNET et. al. | arxiv-cs.CL | 2022-12-15 |
1403 | Local Self-attention in Transformer for Visual Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
XIANG SHEN et. al. | Applied Intelligence | 2022-12-15 |
1404 | Build-a-Bot: Teaching Conversational AI Using A Transformer-Based Intent Recognition and Question Answering Architecture Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The primary concern of this paper is the creation of an interface for students to learn the principles of artificial intelligence by using a natural language pipeline to train a customized model to answer questions based on their own school curriculums. |
Kate Pearce; Sharifa Alghowinem; Cynthia Breazeal; | arxiv-cs.CL | 2022-12-14 |
1405 | DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose N-to-N QA extraction task in which the derived questions and corresponding answers might be separated across different utterances. |
XIN ZHENG et. al. | arxiv-cs.CL | 2022-12-14 |
1406 | BigText-QA: Question Answering Over A Large-Scale Hybrid Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While these KBs offer a structured knowledge representation, they lack the contextual diversity found in natural-language sources. To address this limitation, BigText-QA introduces an integrated QA approach, which is able to answer questions based on a more redundant form of a knowledge graph (KG) that organizes both structured and unstructured (i.e., hybrid) knowledge in a unified graphical representation. |
Jingjing Xu; Maria Biryukov; Martin Theobald; Vinu Ellampallil Venugopal; | arxiv-cs.CL | 2022-12-12 |
1407 | Momentum Contrastive Pre-training for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing pre-training methods for extractive Question Answering (QA) generate cloze-like queries different from natural questions in syntax structure, which could overfit pre-trained models to simple keyword matching. In order to address this problem, we propose a novel Momentum Contrastive pRe-training fOr queStion anSwering (MCROSS) method for extractive QA. |
Minda Hu; Muzhi Li; Yasheng Wang; Irwin King; | arxiv-cs.CL | 2022-12-12 |
1408 | The Turing Deception Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, and overall quality. |
David Noever; Matt Ciolino; | arxiv-cs.LG | 2022-12-09 |
1409 | Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present VideoCoCa that reuses a pretrained image-text contrastive captioner (CoCa) model and adapt it to video-text tasks with minimal extra training. |
SHEN YAN et. al. | arxiv-cs.CV | 2022-12-09 |
1410 | Feasibility Study of A BERT-based Question Answering Chatbot for Information Retrieval from Construction Specifications Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Checking construction specification in every construction phase is critical to ensure proper construction quality and to avoid contractual problems. However, manual review is … |
J. Kim; S. Chung; S. Moon; S. Chi; | 2022 IEEE International Conference on Industrial … | 2022-12-07 |
1411 | Dataset Versus Reality: Understanding Model Performance from The Perspective of Information Need Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Deep learning technologies have brought us many models that outperform human beings on a few benchmarks. An interesting question is: can these models well solve real‐world … |
Mengying Yu; Aixin Sun; | Journal of the Association for Information Science and … | 2022-12-06 |
1412 | MHKD-MVQA: Multimodal Hierarchical Knowledge Distillation for Medical Visual Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Medical Visual Question Answering (VQA) has emerged as a promising solution to enhance clinic-decision making and patient interactions. Given a medical image and a corresponding … |
JIANFENG WANG et. al. | 2022 IEEE International Conference on Bioinformatics and … | 2022-12-06 |
1413 | Dataset Vs Reality: Understanding Model Performance from The Perspective of Information Need Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Deep learning technologies have brought us many models that outperform human beings on a few benchmarks. An interesting question is: can these models well solve real-world problems with similar settings (e.g., identical input/output) to the benchmark datasets? |
Mengying Yu; Aixin Sun; | arxiv-cs.IR | 2022-12-05 |
1414 | QBERT: Generalist Model for Processing Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces QBERT, a generalist model for processing questions. |
Zhaozhen Xu; Nello Cristianini; | arxiv-cs.CL | 2022-12-04 |
1415 | Applying Multilingual Models to Question Answering (QA) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop models for the tasks of (1) determining if a question is answerable given the context and (2) identifying the answer texts within the context using IOB tagging. |
Ayrton San Joaquin; Filip Skubacz; | arxiv-cs.CL | 2022-12-04 |
1416 | Query-Driven Knowledge Base Completion Using Multimodal Path Fusion Over Multimodal Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a multimodal path fusion algorithm to rank candidate answers based on different paths in the multimodal knowledge graphs, achieving much better performance than question answering, rule inference and a baseline fusion algorithm. |
Yang Peng; Daisy Zhe Wang; | arxiv-cs.DB | 2022-12-04 |
1417 | Retrieval As Attention: End-to-end Learning of Retrieval and Reading Within A Single Transformer IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Retrievers and readers are usually modeled separately, which necessitates a cumbersome implementation and is hard to train and adapt in an end-to-end fashion. In this paper, we revisit this design and eschew the separate architecture and training in favor of a single Transformer that performs Retrieval as Attention (ReAtt), and end-to-end training solely based on supervision from the end QA task. |
ZHENGBAO JIANG et. al. | arxiv-cs.CL | 2022-12-04 |
1418 | Compound Tokens: Channel Fusion for Vision-Language Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present an effective method for fusing visual-and-language representations for several question answering tasks including visual question answering and visual entailment. |
Maxwell Mbabilla Aladago; AJ Piergiovanni; | arxiv-cs.CV | 2022-12-02 |
1419 | Relation-Aware Language-Graph Transformer for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, most existing GNN-based modules for QA do not take advantage of rich relational information of KGs and depend on limited information interaction between the LM and the KG. To address these issues, we propose Question Answering Transformer (QAT), which is designed to jointly reason over language and graphs with respect to entity relations in a unified manner. |
JINYOUNG PARK et. al. | arxiv-cs.CL | 2022-12-02 |
1420 | UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning. |
Jinhao Jiang; Kun Zhou; Wayne Xin Zhao; Ji-Rong Wen; | arxiv-cs.CL | 2022-12-01 |
1421 | Gender Screening on Question-answering Communities Related Papers Related Patents Related Grants Related Venues Related Experts View |
A. Figueroa; B. Peralta; O. Nicolis; | Expert Syst. Appl. | 2022-12-01 |
1422 | Learning to Select from Multiple Options Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our methods are evaluated on three tasks (ultra-fine entity typing, intent detection and multi-choice QA) that are typical selection problems with different sizes of options. |
Jiangshu Du; Wenpeng Yin; Congying Xia; Philip S. Yu; | arxiv-cs.CL | 2022-12-01 |
1423 | Multi-perspective Respondent Representations for Answer Ranking in Community Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Lingfei Qian; Jian Wang; Hongfei Lin; Liang Yang; | Inf. Sci. | 2022-12-01 |
1424 | A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We demonstrate that synthetic domain-specific datasets can be generated easily using domain-general models, while still providing significant improvements to QA performance. We present two new tools for this task: A flexible pipeline for validating the synthetic QA data and training downstream models on it, and an online interface to facilitate human annotation of this generated data. |
Matthew Maufe; James Ravenscroft; Rob Procter; Maria Liakata; | arxiv-cs.CL | 2022-11-30 |
1425 | CREPE: Open-Domain Question Answering with False Presuppositions IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce CREPE, a QA dataset containing a natural distribution of presupposition failures from online information-seeking forums. |
Xinyan Velocity Yu; Sewon Min; Luke Zettlemoyer; Hannaneh Hajishirzi; | arxiv-cs.CL | 2022-11-30 |
1426 | Penalizing Confident Predictions on Largely Perturbed Inputs Does Not Improve Out-of-Distribution Generalization in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Question answering (QA) models are shown to be insensitive to large perturbations to inputs; that is, they make correct and confident predictions even when given largely perturbed inputs from which humans can not correctly derive answers. |
Kazutoshi Shinoda; Saku Sugawara; Akiko Aizawa; | arxiv-cs.CL | 2022-11-29 |
1427 | Diverse Multi-Answer Retrieval with Determinantal Point Processes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we address multi-answer retrieval which entails retrieving passages that can capture majority of the diverse answers to the question. |
Poojitha Nandigam; Nikhil Rayaprolu; Manish Shrivastava; | arxiv-cs.CL | 2022-11-29 |
1428 | PiggyBack: Pretrained Visual Question Answering Environment for Backing Up Non-deep Learning Professionals Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a PiggyBack, a Visual Question Answering platform that allows users to apply the state-of-the-art visual-language pretrained models easily. |
ZHIHAO ZHANG et. al. | arxiv-cs.CV | 2022-11-29 |
1429 | Frustratingly Easy Label Projection for Cross-lingual Transfer IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present an extensive empirical study across 57 languages and three tasks (QA, NER, and Event Extraction) to evaluate the effectiveness and limitations of both methods, filling an important gap in the literature. |
Yang Chen; Chao Jiang; Alan Ritter; Wei Xu; | arxiv-cs.CL | 2022-11-28 |
1430 | Improving Low-Resource Question Answering Using Active Learning in Multiple Stages Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work we propose a novel approach that combines data augmentation via question-answer generation with Active Learning to improve performance in low resource settings, where the target domains are diverse in terms of difficulty and similarity to the source domain. |
Maximilian Schmidt; Andrea Bartezzaghi; Jasmina Bogojeska; A. Cristiano I. Malossi; Thang Vu; | arxiv-cs.CL | 2022-11-27 |
1431 | Contrastive Semantic Similarity Learning for Multi-Hop Question Answering Over Event-Centric Knowledge Graphs Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Question answering in natural languages provides an intuitive and efficient way to help people access the rich information stored in various kinds of knowledge graphs (KGs). One … |
Wei Tang; Qingchao Kong; W. Mao; Xiaofei Wu; | 2022 IEEE 8th International Conference on Cloud Computing … | 2022-11-26 |
1432 | Self-Supervised Vision-Language Pretraining for Medial Visual Question Answering IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Medical image visual question answering (VQA) is a task to answer clinical questions, given a radiographic image, which is a challenging problem that requires a model to integrate … |
Pengfei Li; Gang Liu; Lin Tan; Jinying Liao; Shenjun Zhong; | 2023 IEEE 20th International Symposium on Biomedical … | 2022-11-24 |
1433 | TSGP: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous methods typically retrieved from traditional knowledge bases or used pre-trained language models (PrLMs) to generate fixed types of knowledge, which have poor generalization ability. In this paper, we aim to address the above limitation by leveraging the implicit knowledge stored in PrLMs and propose a two-stage prompt-based unsupervised commonsense question answering framework (TSGP). |
Yueqing Sun; Yu Zhang; Le Qi; Qi Shi; | arxiv-cs.CL | 2022-11-24 |
1434 | Question Answering and Question Generation for Finnish Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present the first neural QA and QG models that work with Finnish. |
Ilmari Kylliäinen; Roman Yangarber; | arxiv-cs.CL | 2022-11-24 |
1435 | Self-supervised Vision-language Pretraining for Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a self-supervised method that applies Masked image modeling, Masked language modeling, Image text matching and Image text alignment via contrastive learning (M2I2) for pretraining on medical image caption dataset, and finetunes to downstream medical VQA tasks. |
Pengfei Li; Gang Liu; Lin Tan; Jinying Liao; Shenjun Zhong; | arxiv-cs.CV | 2022-11-24 |
1436 | Look, Read and Ask: Learning to Ask Questions By Reading Text in Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address TextVQG, we present an OCR consistent visual question generation model that Looks into the visual content, Reads the scene text, and Asks a relevant and meaningful natural language question. |
Soumya Jahagirdar; Shankar Gangisetty; Anand Mishra; | arxiv-cs.CV | 2022-11-23 |
1437 | Can Open-Domain QA Reader Utilize External Knowledge Efficiently Like Humans? IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by this procedure, we ask a research question Can the open-domain QA reader utilize external knowledge efficiently like humans without sacrificing the prediction performance? Driven by this question, we explore an approach that utilizes both ‘closed-book’ (leveraging knowledge already present in the model parameters) and ‘open-book’ inference (leveraging external knowledge). |
Neeraj Varshney; Man Luo; Chitta Baral; | arxiv-cs.CL | 2022-11-23 |
1438 | Cross-Modal Contrastive Learning for Robust Reasoning in VQA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a simple but effective cross-modal contrastive learning strategy to get rid of the shortcut reasoning caused by imbalanced annotations and improve the overall performance. |
Qi Zheng; Chaoyue Wang; Daqing Liu; Dadong Wang; Dacheng Tao; | arxiv-cs.CV | 2022-11-21 |
1439 | Enhancing Self-Consistency and Performance of Pre-Trained Language Models Through Natural Language Inference IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: . To address this failure mode, we propose a framework, Consistency Correction through Relation Detection, or ConCoRD, for boosting the consistency and accuracy of pre-trained NLP models using pre-trained natural language inference (NLI) models without fine-tuning or re-training. |
ERIC MITCHELL et. al. | arxiv-cs.CL | 2022-11-21 |
1440 | CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most existing continual learning (CL) research concentrates on unimodal tasks, whereas a more practical application scenario, i.e, CL on cross-domain VQA, has not been studied. Motivated by this, we introduce CL-CrossVQA, a rigorous Continual Learning benchmark for Cross-domain Visual Question Answering, through which we conduct extensive experiments on 4 VLPMs, 4 CL approaches, and 5 VQA datasets from different domains. |
YAO ZHANG et. al. | arxiv-cs.CV | 2022-11-18 |
1441 | Open-Domain Conversational Question Answering with Historical Answers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes ConvADR-QA that leverages historical answers to boost retrieval performance and further achieves better answering performance. |
Hung-Chieh Fang; Kuo-Han Hung; Chao-Wei Huang; Yun-Nung Chen; | arxiv-cs.CL | 2022-11-17 |
1442 | Unified Question Answering in Slovene Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We adapt a successful English question-answering approach, called UnifiedQA, to the less-resourced Slovene language. |
Katja Logar; Marko Robnik-Šikonja; | arxiv-cs.CL | 2022-11-16 |
1443 | A Comparative Study of Question Answering Over Knowledge Bases Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this article, we provide a comparative study of six representative KBQA systems on eight benchmark datasets. |
KHIEM VINH TRAN et. al. | arxiv-cs.CL | 2022-11-15 |
1444 | QAmeleon: Multilingual QA with Only 5 Examples IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: An alternative to building large monolingual training datasets is to leverage pre-trained language models (PLMs) under a few-shot learning setting. |
PRIYANKA AGRAWAL et. al. | arxiv-cs.CL | 2022-11-15 |
1445 | Large Language Models Struggle to Learn Long-Tail Knowledge IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we study the relationship between the knowledge memorized by large language models and the information in pre-training datasets scraped from the web. |
Nikhil Kandpal; Haikang Deng; Adam Roberts; Eric Wallace; Colin Raffel; | arxiv-cs.CL | 2022-11-15 |
1446 | Empowering Language Models with Knowledge Graph Reasoning for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose knOwledge REasOning empowered Language Model (OREO-LM), which consists of a novel Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. |
ZINIU HU et. al. | arxiv-cs.CL | 2022-11-15 |
1447 | Generative Long-form Question Answering: Relevance, Faithfulness and Succinctness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this thesis, we investigated the relevance, faithfulness, and succinctness aspects of Long Form Question Answering (LFQA). |
Dan Su; | arxiv-cs.CL | 2022-11-15 |
1448 | MapQA: A Dataset for Question Answering on Choropleth Maps Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, little work has paid attention to understanding maps; general VQA models, and ChartQA models, suffer when asked to perform this task. To facilitate and encourage research in this area, we present MapQA, a large-scale dataset of ~800K question-answer pairs over ~60K map images. |
Shuaichen Chang; David Palzer; Jialin Li; Eric Fosler-Lussier; Ningchuan Xiao; | arxiv-cs.CV | 2022-11-15 |
1449 | PromptCap: Prompt-Guided Task-Aware Image Captioning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Generic image captions often miss visual details essential for the LM to answer visual questions correctly. To address this challenge, we propose PromptCap (Prompt-guided image Captioning), a captioning model designed to serve as a better connector between images and black-box LMs. |
YUSHI HU et. al. | arxiv-cs.CV | 2022-11-15 |
1450 | Learning to Answer Multilingual and Code-Mixed Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this dissertation, we focus on advancing QA techniques for handling end-user queries in multilingual environments. |
Deepak Gupta; | arxiv-cs.CL | 2022-11-14 |
1451 | Multi-VQG: Generating Engaging Questions for Multiple Images Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose generating engaging questions from multiple images. |
Min-Hsuan Yeh; Vicent Chen; Ting-Hao ‘Kenneth’ Haung; Lun-Wei Ku; | arxiv-cs.CL | 2022-11-14 |
1452 | ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers (ALBERT) with knowledge graph information extraction technique. |
Byeongmin Choi; YongHyun Lee; Yeunwoong Kyung; Eunchan Kim; | arxiv-cs.CL | 2022-11-13 |
1453 | Knowledge Base Completion Using Web-Based Question Answering and Multimodal Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these knowledge bases are highly incomplete. To solve this problem, we propose a web-based question answering system system with multimodal fusion of unstructured and structured information, to fill in missing information for knowledge bases. |
Yang Peng; Daisy Zhe Wang; | arxiv-cs.AI | 2022-11-13 |
1454 | Mining Mathematical Documents for Question Answering Via Unsupervised Formula Labeling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we aim to bridge the gap by presenting data mining methods and benchmark results to employ Mathematical Entity Linking (MathEL) and Unsupervised Formula Labeling (UFL) for semantic formula search and mathematical question answering (MathQA) on the arXiv preprint repository, Wikipedia, and Wikidata, which is part of the Wikimedia ecosystem of free knowledge. |
Philipp Scharpf; Moritz Schubotz; Bela Gipp; | arxiv-cs.IR | 2022-11-12 |
1455 | Overcoming Language Priors with Self-contrastive Learning for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Hong Yan; Lijun Liu; Xupeng Feng; Qingsong Huang; | Multimedia Tools and Applications | 2022-11-10 |
1456 | MF2-MVQA: A Multi-stage Feature Fusion Method for Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a simple but powerful multi-stage feature fusion method, MF2-MVQA, which stage-wise fuses multi-level visual features with textual semantics. |
Shanshan Song; Jiangyun Li; Jing Wang; Yuanxiu Cai; Wenkai Dong; | arxiv-cs.CV | 2022-11-10 |
1457 | Watching The News: Towards VideoQA Models That Can Read Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel VideoQA task that requires reading and understanding the text in the video. |
Soumya Jahagirdar; Minesh Mathew; Dimosthenis Karatzas; C. V. Jawahar; | arxiv-cs.CV | 2022-11-10 |
1458 | Biomedical Multi-hop Question Answering Using Knowledge Graph Embeddings and Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we use a publicly available KG called Hetionet which is an integrative network of biomedical knowledge assembled from 29 different databases of genes, compounds, diseases, and more. |
Dattaraj J. Rao; Shraddha S. Mane; Mukta A. Paliwal; | arxiv-cs.AI | 2022-11-10 |
1459 | Toward A Neural Semantic Parsing System for EHR Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The recent advancements in neural SP show a promise for building a robust and flexible semantic parser without much human effort. Thus, in this paper, we aim to systematically assess the performance of two such neural SP models for EHR question answering (QA). |
Sarvesh Soni; Kirk Roberts; | arxiv-cs.CL | 2022-11-08 |
1460 | COV19IR : COVID-19 Domain Literature Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on transformer neural network, we provided solutions to implement the tasks on CORD-19 dataset, we display some examples to show the effectiveness of our proposed solutions. |
Arusarka Bose; Zili Zhou; Guandong Xu; | arxiv-cs.IR | 2022-11-08 |
1461 | NAPG: Non-Autoregressive Program Generation for Hybrid Tabular-Textual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a non-autoregressive program generation framework, which independently generates complete program tuples containing both operators and operands, can address the error propagation issue while significantly boosting the speed of program generation. |
Tengxun Zhang; Hongfei Xu; Josef van Genabith; Deyi Xiong; Hongying Zan; | arxiv-cs.CL | 2022-11-07 |
1462 | Conversational Agents for Information Retrieval in The Education Domain Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Text-based conversational agents (CAs) are widely deployed across a number of daily tasks, including information retrieval. However, most existing agents follow a default design … |
Anuschka Schmitt; Thiemo Wambsganss; J. Leimeister; | Proceedings of the ACM on Human-Computer Interaction | 2022-11-07 |
1463 | Zero-Shot Video Question Answering Via Frozen Bidirectional Language Models IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a framework based on frozen bidirectional masked language models to tackle zero-shot video question answering. |
Antoine Yang; Antoine Miech; Josef Sivic; Ivan Laptev; Cordelia Schmid; | nips | 2022-11-06 |
1464 | Towards Video Text Visual Question Answering: Benchmark and Baseline IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a new task named Video Text Visual Question Answering (ViteVQA in short) that aims at answering questions by reasoning texts and visual information spatiotemporally in a given video. |
MINYI ZHAO et. al. | nips | 2022-11-06 |
1465 | Towards Improving Faithfulness in Abstractive Summarization IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a Faithfulness Enhanced Summarization model (FES), which is designed for addressing these two problems and improving faithfulness in abstractive summarization. |
Xiuying Chen; Mingzhe Li; Xin Gao; Xiangliang Zhang; | nips | 2022-11-06 |
1466 | Learn to Explain: Multimodal Reasoning Via Thought Chains for Science Question Answering IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we present Science Question Answering (SQA), a new benchmark that consists of ~21k multimodal multiple choice questions with a diverse set of science topics and annotations of their answers with corresponding lectures and explanations. |
PAN LU et. al. | nips | 2022-11-06 |
1467 | Flamingo: A Visual Language Model for Few-Shot Learning IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family of Visual Language Models (VLM) with this ability. |
JEAN-BAPTISTE ALAYRAC et. al. | nips | 2022-11-06 |
1468 | EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose an automated reward shaping method for guiding exploration in instruction following settings. |
Thomas Carta; Pierre-Yves Oudeyer; Olivier Sigaud; Sylvain Lamprier; | nips | 2022-11-06 |
1469 | REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, we observe in most state-of-the-art knowledge-based VQA methods: 1) visual features are extracted either from the whole image or in a sliding window manner for retrieving knowledge, and the important relationship within/among object regions is neglected; 2) visual features are not well utilized in the final answering model, which is counter-intuitive to some extent. Based on these observations, we propose a new knowledge-based VQA method REVIVE, which tries to utilize the explicit information of object regions not only in the knowledge retrieval stage but also in the answering model. |
YUANZE LIN et. al. | nips | 2022-11-06 |
1470 | Deep Bidirectional Language-Knowledge Graph Pretraining IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here we propose DRAGON (Deep Bidirectional Language-Knowledge Graph Pretraining), a self-supervised approach to pretraining a deeply joint language-knowledge model from raw text and KG at scale. |
MICHIHIRO YASUNAGA et. al. | nips | 2022-11-06 |
1471 | Improving and Evaluating Complex Question Answering Over Knowledge Bases By Constructing Strongly Supervised Data Related Papers Related Patents Related Grants Related Venues Related Experts View |
Xing Cao; Yingsi Zhao; Bo Shen; | Neural Computing and Applications | 2022-11-05 |
1472 | Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 2022 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce efficient deep learning-based methods for legal document processing including Legal Document Retrieval and Legal Question Answering tasks in the Automated Legal Question Answering Competition (ALQAC 2022). |
Hieu Nguyen Van; Dat Nguyen; Phuong Minh Nguyen; Minh Le Nguyen; | arxiv-cs.CL | 2022-11-03 |
1473 | RQUGE: Reference-Free Metric for Evaluating Question Generation By Answering The Question IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new metric, RQUGE, based on the answerability of the candidate question given the context. |
ALIREZA MOHAMMADSHAHI et. al. | arxiv-cs.CL | 2022-11-02 |
1474 | Remote Sensing Visual Question Answering with A Self-attention Multi-modal Encoder Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Visual Question Answering (VQA) on remote sensing imagery can help non-expert users in extracting information from Earth observation data. Current approaches follow a neural … |
João Daniel Silva; João Magalhães; D. Tuia; Bruno Martins; | Proceedings of the 5th ACM SIGSPATIAL International … | 2022-11-01 |
1475 | Incorporating Anticipation Embedding Into Reinforcement Learning Framework for Multi-hop Knowledge Graph Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
HAI CUI et. al. | Inf. Sci. | 2022-11-01 |
1476 | Predicting The Quality of Answers with Less Bias in Online Health Question Answering Communities Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yan Qiu; Shuai Ding; Di Tian; Caiyun Zhang; Dian Zhou; | Inf. Process. Manag. | 2022-11-01 |
1477 | HyperKGQA: Question Answering Over Knowledge Graphs Using Hyperbolic Representation Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Knowledge Graph Question Answering (KGQA) models enable users to acquire entity-based answers from a Knowledge Graph by asking natural language questions (NLQs) without the need … |
Nadya Abdel Madjid; Ola El Khatib; Shuang Gao; D. Difallah; | 2022 IEEE International Conference on Data Mining (ICDM) | 2022-11-01 |
1478 | Query Refinement Prompts for Closed-Book Long-Form Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Large language models (LLMs) have been shown to perform well in answering questions and in producing long-form texts, both in few-shot closed-book settings. |
Reinald Kim Amplayo; Kellie Webster; Michael Collins; Dipanjan Das; Shashi Narayan; | arxiv-cs.CL | 2022-10-31 |
1479 | Towards Zero-Shot and Few-Shot Table Question Answering Using GPT-3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present very early results on using GPT-3 to perform question answering on tabular data. |
Pragya Srivastava; Tanuja Ganu; Saikat Guha; | arxiv-cs.LG | 2022-10-31 |
1480 | A Multi-level Mesh Mutual Attention Model for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zhi Lei; Guixian Zhang; Lijuan Wu; Kui Zhang; Rongjiao Liang; | Data Science and Engineering | 2022-10-30 |
1481 | Contrastive Representation Learning for Conversational Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work proposes a contrastive representation learning-based approach to rank KG paths effectively. |
Endri Kacupaj; Kuldeep Singh; Maria Maleshkova; Jens Lehmann; | cikm | 2022-10-29 |
1482 | ExpertBert: Pretraining Expert Finding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an expert-level pretraining language model named ExpertBert, aiming to model questions, experts as well as question-expert matching effectively in a pretraining manner. |
Hongtao Liu; Zhepeng Lv; Qing Yang; Dongliang Xu; Qiyao Peng; | cikm | 2022-10-29 |
1483 | ChiQA: A Large Scale Image-based Real-World Question Answering Dataset for Multi-Modal Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a new question answering dataset based on image-ChiQA. |
BINGNING WANG et. al. | cikm | 2022-10-29 |
1484 | WDRASS: A Web-scale Dataset for Document Retrieval and Answer Sentence Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present WDRASS, a dataset for ODQA based on answer sentence selection (AS2) models, which consider sentences as candidate answers for QA systems. |
Zeyu Zhang; Thuy Vu; Sunil Gandhi; Ankit Chadha; Alessandro Moschitti; | cikm | 2022-10-29 |
1485 | Unanswerable Question Correction and Explanation Over Personal Knowledge Base Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we seek not only to correct unanswerable questions based on a personal knowledge base, but also to explain the reason of the correction. |
An-Zi Yen; Hen-Hsen Huang; Hsin-Hsi Chen; | cikm | 2022-10-29 |
1486 | On The Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Interacting with a speech interface to query a Question Answering (QA) system is becoming increasingly popular. Typically, QA systems rely on passage retrieval to select candidate … |
Georgios Sidiropoulos; Svitlana Vakulenko; Evangelos Kanoulas; | cikm | 2022-10-29 |
1487 | Reinforced Question Rewriting for Conversational Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we propose using QA feedback to supervise the rewriting model with reinforcement learning. |
ZHIYU CHEN et. al. | arxiv-cs.CL | 2022-10-27 |
1488 | Socially Interactive Agent Dialogue Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Professor David Traum, from the University of Southern California. DAVID: Um, hi, glad to be here! SIENNA: Welcome, I have a lot of questions about dialogue! SILA: I thought … |
D. Traum; | The Handbook on Socially Interactive Agents | 2022-10-27 |
1489 | Look to The Right: Mitigating Relative Position Bias in Extractive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we discovered that the relative position of an answer, which is defined as the relative distance from an answer span to the closest question-context overlap word, can be exploited by QA models as superficial cues for making predictions. |
Kazutoshi Shinoda; Saku Sugawara; Akiko Aizawa; | arxiv-cs.CL | 2022-10-26 |
1490 | DyREx: Dynamic Query Representation for Extractive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: These query vectors lack the context of the inputs, which can be a bottleneck for the model performance. To address this problem, we propose \textit{DyREx}, a generalization of the \textit{vanilla} approach where we dynamically compute query vectors given the input, using an attention mechanism through transformer layers. |
URCHADE ZARATIANA et. al. | arxiv-cs.CL | 2022-10-26 |
1491 | What’s Different Between Visual Question Answering for Machine Understanding Versus for Accessibility? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most of the existing benchmarking datasets for VQA focus on machine understanding and it remains unclear how progress on those datasets corresponds to improvements in this real-world use case. We aim to answer this question by evaluating discrepancies between machine understanding datasets (VQA-v2) and accessibility datasets (VizWiz) by evaluating a variety of VQA models. |
Yang Trista Cao; Kyle Seelman; Kyungjun Lee; Hal Daumé III; | arxiv-cs.CL | 2022-10-26 |
1492 | CS1QA: A Dataset for Assisting Code-based Question Answering in An Introductory Programming Course Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce CS1QA, a dataset for code-based question answering in the programming education domain. |
Changyoon Lee; Yeon Seonwoo; Alice Oh; | arxiv-cs.CL | 2022-10-26 |
1493 | What’s Different Between Visual Question Answering for Machine “Understanding” Versus for Accessibility? Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In visual question answering (VQA), a machine must answer a question given an associated image. Recently, accessibility researchers have explored whether VQA can be deployed in a … |
Yang Trista Cao; Kyle Seelman; Kyungjun Lee; Hal Daum’e; | ArXiv | 2022-10-26 |
1494 | RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce RoMQA, the first benchmark for robust, multi-evidence, multi-answer question answering (QA). |
Victor Zhong; Weijia Shi; Wen-tau Yih; Luke Zettlemoyer; | arxiv-cs.CL | 2022-10-25 |
1495 | Speeding Up Question Answering Task of Language Models Via Inverted Index Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we leverage an inverted indexing mechanism combined with LLMs to improve the efficiency of question-answering models for closed-domain questions. |
Xiang Ji; Yesim Sungu-Eryilmaz; Elaheh Momeni; Reza Rawassizadeh; | arxiv-cs.CL | 2022-10-24 |
1496 | Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel method to synthesize data for CQA with various question types, including open-ended, closed-ended, and unanswerable questions. |
Seonjeong Hwang; Yunsu Kim; Gary Geunbae Lee; | arxiv-cs.CL | 2022-10-24 |
1497 | VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we focus on questions that require commonsense reasoning. |
Sahithya Ravi; Aditya Chinchure; Leonid Sigal; Renjie Liao; Vered Shwartz; | arxiv-cs.CV | 2022-10-24 |
1498 | ReaRev: Adaptive Reasoning for Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our method, termed ReaRev, introduces a new way to KGQA reasoning with respect to both instruction decoding and execution. |
Costas Mavromatis; George Karypis; | arxiv-cs.CL | 2022-10-24 |
1499 | Event-Centric Question Answering Via Contrastive Learning and Invertible Event Transformation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR. |
Junru Lu; Xingwei Tan; Gabriele Pergola; Lin Gui; Yulan He; | arxiv-cs.CL | 2022-10-23 |
1500 | Leveraging Large Language Models for Multiple Choice Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This approach allows the model to explicitly compare answer options, reduces computational costs, and mitigates the effects of tokenization scheme and answer option representations on answer selection. |
Joshua Robinson; Christopher Michael Rytting; David Wingate; | arxiv-cs.CL | 2022-10-22 |