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 | Contextual Breach: Assessing The Robustness of Transformer-based QA Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a unique dataset that incorporates seven distinct types of adversarial noise into the context, each applied at five different intensity levels on the SQuAD dataset. |
Asir Saadat; Nahian Ibn Asad; Md Farhan Ishmam; | arxiv-cs.CL | 2024-09-17 |
| 2 | OneEncoder: A Lightweight Framework for Progressive Alignment of Modalities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This approach has limitations: (i) it is very expensive due to the need for training large encoders on extensive datasets, (ii) acquiring aligned large paired datasets is challenging, and (iii) adding new modalities requires retraining the entire framework to incorporate these modalities. To address these issues, we propose OneEncoder, a lightweight framework that progressively represents and aligns four modalities (image, text, audio, video). |
Bilal Faye; Hanane Azzag; Mustapha Lebbah; | arxiv-cs.CV | 2024-09-17 |
| 3 | HALO: Hallucination Analysis and Learning Optimization to Empower LLMs with Retrieval-Augmented Context for Guided Clinical Decision Making Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces HALO, a novel framework designed to enhance the accuracy and reliability of medical question-answering (QA) systems by focusing on the detection and mitigation of hallucinations. |
SUMERA ANJUM et. al. | arxiv-cs.CL | 2024-09-16 |
| 4 | StruEdit: Structured Outputs Enable The Fast and Accurate Knowledge Editing for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We argue that these challenges stem from the unstructured nature of natural language outputs. To address the above challenges, we propose $\textbf{Stru}$ctural $\textbf{Edit}$ing ($\textbf{StruEdit}$), an improved baseline for knowledge editing. |
BAOLONG BI et. al. | arxiv-cs.CL | 2024-09-16 |
| 5 | A Benchmark Dataset with Larger Context for Non-Factoid Question Answering Over Islamic Text Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Yet, the scarcity of QA systems tailored specifically to the detailed nature of inquiries about the Quranic Tafsir (explanation, interpretation, context of Quran for clarity) and Ahadith poses significant challenges. To address this gap, we introduce a comprehensive dataset meticulously crafted for QA purposes within the domain of Quranic Tafsir and Ahadith. |
Faiza Qamar; Seemab Latif; Rabia Latif; | arxiv-cs.CL | 2024-09-15 |
| 6 | QTG-VQA: Question-Type-Guided Architectural for VideoQA Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Particularly, considering the significant variation in dependency on temporal information across different question types, and given that the representation of such information coincidentally represents a principal challenge and difficulty for VideoQA as opposed to ImageQA. To address these challenges, we propose QTG-VQA, a novel architecture that incorporates question-type-guided attention and adaptive learning mechanism. |
Zhixian He; Pengcheng Zhao; Fuwei Zhang; Shujin Lin; | arxiv-cs.CV | 2024-09-14 |
| 7 | Contri(e)ve: Context + Retrieve for Scholarly Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a two step solution using open source Large Language Model(LLM): Llama3.1 for Scholarly-QALD dataset. |
Kanchan Shivashankar; Nadine Steinmetz; | arxiv-cs.IR | 2024-09-13 |
| 8 | Guiding Vision-Language Model Selection for Visual Question-Answering Across Tasks, Domains, and Knowledge Types Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a comprehensive framework for evaluating VLMs tailored to VQA tasks in practical settings. |
Neelabh Sinha; Vinija Jain; Aman Chadha; | arxiv-cs.CV | 2024-09-13 |
| 9 | Electrocardiogram Report Generation and Question Answering Via Retrieval-Augmented Self-Supervised Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Interpreting electrocardiograms (ECGs) and generating comprehensive reports remain challenging tasks in cardiology, often requiring specialized expertise and significant time investment. To address these critical issues, we propose ECG-ReGen, a retrieval-based approach for ECG-to-text report generation and question answering. |
Jialu Tang; Tong Xia; Yuan Lu; Cecilia Mascolo; Aaqib Saeed; | arxiv-cs.LG | 2024-09-13 |
| 10 | QueryCAD: Grounded Question Answering for CAD Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these models are rarely considered in novel AI-based approaches, such as the automatic synthesis of robot programs, as there are no readily available methods that would allow CAD models to be incorporated for the analysis, interpretation, or extraction of information. To address these limitations, we propose QueryCAD, the first system designed for CAD question answering, enabling the extraction of precise information from CAD models using natural language queries. |
Claudius Kienle; Benjamin Alt; Darko Katic; Rainer Jäkel; | arxiv-cs.RO | 2024-09-13 |
| 11 | L3Cube-IndicQuest: A Benchmark Questing Answering Dataset for Evaluating Knowledge of LLMs in Indic Context Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present the L3Cube-IndicQuest, a gold-standard question-answering benchmark dataset designed to evaluate how well multilingual LLMs capture regional knowledge across various Indic languages. |
Pritika Rohera; Chaitrali Ginimav; Akanksha Salunke; Gayatri Sawant; Raviraj Joshi; | arxiv-cs.CL | 2024-09-13 |
| 12 | Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Source2Synth: a new method that can be used for teaching LLMs new skills without relying on costly human annotations. |
ALISIA LUPIDI et. al. | arxiv-cs.CL | 2024-09-12 |
| 13 | Top-down Activity Representation Learning for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, to leverage the spatial visual context representation capability of the CLIP model for obtaining non-continuous visual representations in terms of contextual events in videos, we convert long-term video sequences into a spatial image domain and finetune the multimodal model LLaVA for the VideoQA task. |
Yanan Wang; Shuichiro Haruta; Donghuo Zeng; Julio Vizcarra; Mori Kurokawa; | arxiv-cs.CV | 2024-09-12 |
| 14 | Multi-object Event Graph Representation Learning for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While prior works have focused on modeling individual object movements using transformer-based methods, they falter when capturing complex scenarios involving multiple objects (e.g., a boy is throwing a ball in a hoop). We propose a contrastive language event graph representation learning method called CLanG to address this limitation. |
Yanan Wang; Shuichiro Haruta; Donghuo Zeng; Julio Vizcarra; Mori Kurokawa; | arxiv-cs.CV | 2024-09-12 |
| 15 | AdaCAD: Adaptively Decoding to Balance Conflicts Between Contextual and Parametric Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a fine-grained, instance-level approach called AdaCAD, which dynamically infers the weight of adjustment based on the degree of conflict, as measured by the Jensen-Shannon divergence between distributions representing contextual and parametric knowledge. |
Han Wang; Archiki Prasad; Elias Stengel-Eskin; Mohit Bansal; | arxiv-cs.CL | 2024-09-11 |
| 16 | Learning to Compress Contexts for Efficient Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous works like Retrival-Augmented VQA-v2 (RAVQA-v2) focus on utilizing as much input information, such as image-based textual descriptions and retrieved knowledge, as possible to improve performance, but they all overlook the issue that with the number of input tokens increasing, inference efficiency significantly decreases, which contradicts the demands of practical applications. To address this issue, we propose Retrieval-Augmented MLLM with Compressed Contexts (RACC). |
WEIXI WENG et. al. | arxiv-cs.CV | 2024-09-11 |
| 17 | Experimenting with Legal AI Solutions: The Case of Question-Answering for Access to Justice Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a human-centric legal NLP pipeline, covering data sourcing, inference, and evaluation. |
Jonathan Li; Rohan Bhambhoria; Samuel Dahan; Xiaodan Zhu; | arxiv-cs.CL | 2024-09-11 |
| 18 | Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While Large Audio Language Models excel in general audio understanding, they are limited in temporal reasoning which may hinder their commercial applications and on device deployment. This paper addresses these challenges and limitations in audio temporal reasoning. |
Arvind Krishna Sridhar; Yinyi Guo; Erik Visser; | arxiv-cs.SD | 2024-09-10 |
| 19 | Towards Building A Robust Knowledge Intensive Question Answering Model with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the issue of model accuracy decline caused by noisy external information, we propose a data augmentation-based fine-tuning method to enhance LLM’s robustness against noise. |
Hong Xingyun Hong; Shao Yan Shao; Wang Zhilin Wang; Duan Manni Duan; Jin Xiongnan; | arxiv-cs.CL | 2024-09-09 |
| 20 | Seek and Solve Reasoning for Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by how humans solve TQA tasks, we propose a Seek-and-Solve pipeline that instructs the LLM to first seek relevant information and then answer questions. |
Ruya Jiang; Chun Wang; Weihong Deng; | arxiv-cs.CL | 2024-09-08 |
| 21 | Combining LLMs and Knowledge Graphs to Reduce Hallucinations in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A key issue is the hallucination problem, where models generate information unsupported by the underlying data, potentially leading to dangerous misinformation. This paper presents a novel approach designed to bridge this gap by combining Large Language Models (LLM) and Knowledge Graphs (KG) to improve the accuracy and reliability of question-answering systems, on the example of a biomedical KG. |
Larissa Pusch; Tim O. F. Conrad; | arxiv-cs.CL | 2024-09-06 |
| 22 | COLUMBUS: Evaluating COgnitive Lateral Understanding Through Multiple-choice ReBUSes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Effective problem-solving also necessitates lateral thinking, which remains understudied in AI and has not been used to test visual perception systems. To bridge this gap, we formulate visual lateral thinking as a multiple-choice question-answering task and describe a three-step taxonomy-driven methodology for instantiating task examples. |
Koen Kraaijveld; Yifan Jiang; Kaixin Ma; Filip Ilievski; | arxiv-cs.CV | 2024-09-06 |
| 23 | Question-Answering Dense Video Events Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present question-answering dense video events, a novel task that requires answering and grounding the dense-event questions in long videos, thus challenging MLLMs to faithfully comprehend and reason about multiple events occurring over extended time periods. |
Hangyu Qin; Junbin Xiao; Angela Yao; | arxiv-cs.CV | 2024-09-06 |
| 24 | RAG Based Question-Answering for Contextual Response Prediction System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce an end-to-end framework that employs LLMs with RAG capabilities for industry use cases. |
Sriram Veturi; Saurabh Vaichal; Reshma Lal Jagadheesh; Nafis Irtiza Tripto; Nian Yan; | arxiv-cs.CL | 2024-09-05 |
| 25 | Word and Phrase Features in Graph Convolutional Network for Automatic Question Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional methods, often based on word embeddings and conventional classifiers, struggle to capture the nuanced relationships in natural language, leading to suboptimal performance. To address this, we propose a novel approach leveraging graph convolutional networks (GCNs), named Phrase Question-Graph Convolutional Network (PQ-GCN) to better model the inherent structure of questions. |
Junyoung Lee; Ninad Dixit; Kaustav Chakrabarti; S. Supraja; | arxiv-cs.CL | 2024-09-04 |
| 26 | MARAGS: A Multi-Adapter System for Multi-Task Retrieval Augmented Generation Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we present a multi-adapter retrieval augmented generation system (MARAGS) for Meta’s Comprehensive RAG (CRAG) competition for KDD CUP 2024. |
Mitchell DeHaven; | arxiv-cs.CL | 2024-09-04 |
| 27 | LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we aim to enable long-context LLMs to generate responses with fine-grained sentence-level citations, improving their faithfulness and verifiability. |
JIAJIE ZHANG et. al. | arxiv-cs.CL | 2024-09-04 |
| 28 | R2GQA: Retriever-Reader-Generator Question Answering System to Support Students Understanding Legal Regulations in Higher Education Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this article, we propose the R2GQA system, a Retriever-Reader-Generator Question Answering system, consisting of three main components: Document Retriever, Machine Reader, and Answer Generator. |
Phuc-Tinh Pham Do; Duy-Ngoc Dinh Cao; Khanh Quoc Tran; Kiet Van Nguyen; | arxiv-cs.CL | 2024-09-04 |
| 29 | GoT-CQA: Graph-of-Thought Guided Compositional Reasoning for Chart Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The former refers to answering this question strictly based on the analysis of the visual content or internal data of the given chart, while the latter emphasizes the various logical and numerical reasoning involved in answer prediction process. In this paper, we pay more attention on the complex reasoning in CQA task, and propose a novel Graph-of-Thought (GoT) guided compositional reasoning model called GoT-CQA to overcome this problem. |
LINGLING ZHANG et. al. | arxiv-cs.CV | 2024-09-04 |
| 30 | What Are The Essential Factors in Crafting Effective Long Context Multi-Hop Instruction Datasets? Insights and Best Practices Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To improve the quality of synthetic data, we propose the Multi-agent Interactive Multi-hop Generation (MIMG) framework, incorporating a Quality Verification Agent, a Single-hop Question Generation Agent, a Multiple Question Sampling Strategy, and a Multi-hop Question Merger Agent. |
ZHI CHEN et. al. | arxiv-cs.CL | 2024-09-03 |
| 31 | Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although the iterative RAG approach has been proposed to address this problem, it comes at the cost of significantly reduced efficiency. To address these issues, we propose the diversify-verify-adapt (DIVA) framework. |
YEONJUN IN et. al. | arxiv-cs.CL | 2024-09-03 |
| 32 | Multi-modal Situated Reasoning in 3D Scenes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing datasets and benchmarks for situated understanding are limited in data modality, diversity, scale, and task scope. To address these limitations, we propose Multi-modal Situated Question Answering (MSQA), a large-scale multi-modal situated reasoning dataset, scalably collected leveraging 3D scene graphs and vision-language models (VLMs) across a diverse range of real-world 3D scenes. |
XIONGKUN LINGHU et. al. | arxiv-cs.CV | 2024-09-03 |
| 33 | VProChart: Answering Chart Question Through Visual Perception Alignment Agent and Programmatic Solution Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, chart images are inherently difficult to interpret, and chart-related questions often involve complex logical and numerical reasoning, which hinders the performance of existing models. This paper introduces VProChart, a novel framework designed to address these challenges in CQA by integrating a lightweight Visual Perception Alignment Agent (VPAgent) and a Programmatic Solution Reasoning approach. |
MUYE HUANG et. al. | arxiv-cs.CV | 2024-09-03 |
| 34 | How Privacy-Savvy Are Large Language Models? A Case Study on Compliance and Privacy Technical Review Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper seeks to address this gap by providing a comprehensive case study evaluating LLMs’ performance in privacy-related tasks such as privacy information extraction (PIE), legal and regulatory key point detection (KPD), and question answering (QA) with respect to privacy policies and data protection regulations. We introduce a Privacy Technical Review (PTR) framework, highlighting its role in mitigating privacy risks during the software development life-cycle. |
XICHOU ZHU et. al. | arxiv-cs.CL | 2024-09-03 |
| 35 | CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Corpus Retrieval and Augmentation for Fine-Tuning (CRAFT), a method for generating synthetic datasets, given a small number of user-written few-shots that demonstrate the task to be performed. |
Ingo Ziegler; Abdullatif Köksal; Desmond Elliott; Hinrich Schütze; | arxiv-cs.CL | 2024-09-03 |
| 36 | Kvasir-VQA: A Text-Image Pair GI Tract Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce Kvasir-VQA, an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question-and-answer annotations to facilitate advanced machine learning tasks in Gastrointestinal (GI) diagnostics. |
SUSHANT GAUTAM et. al. | arxiv-cs.CV | 2024-09-02 |
| 37 | Language Models Benefit from Preparation with Elicited Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, some QA tasks hinge more on accessing relevant knowledge than on chaining reasoning steps. We introduce a simple general prompting technique, called PREP, that involves using two instances of LMs: the first (LM1) generates relevant information, and the second (LM2) answers the question based on this information. |
Jiacan Yu; Hannah An; Lenhart K. Schubert; | arxiv-cs.CL | 2024-09-02 |
| 38 | Retrieval-Augmented Natural Language Reasoning for Explainable Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a new VQA-NLE model, ReRe (Retrieval-augmented natural language Reasoning), using leverage retrieval information from the memory to aid in generating accurate answers and persuasive explanations without relying on complex networks and extra datasets. |
Su Hyeon Lim; Minkuk Kim; Hyeon Bae Kim; Seong Tae Kim; | arxiv-cs.CV | 2024-08-30 |
| 39 | MAPWise: Evaluating Vision-Language Models for Advanced Map Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study investigates the efficacy of VLMs in answering questions based on choropleth maps, which are widely used for data analysis and representation. To facilitate and encourage research in this area, we introduce a novel map-based question-answering benchmark, consisting of maps from three geographical regions (United States, India, China), each containing 1000 questions. |
Srija Mukhopadhyay; Abhishek Rajgaria; Prerana Khatiwada; Vivek Gupta; Dan Roth; | arxiv-cs.CV | 2024-08-30 |
| 40 | LLM-Based Multi-Hop Question Answering with Knowledge Graph Integration in Evolving Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods for knowledge editing still face difficulties with multi-hop questions that require accurate fact identification and sequential logical reasoning, particularly among numerous fact updates. To tackle these challenges, this paper introduces Graph Memory-based Editing for Large Language Models (GMeLLo), a straitforward and effective method that merges the explicit knowledge representation of Knowledge Graphs (KGs) with the linguistic flexibility of LLMs. |
RUIRUI CHEN et. al. | arxiv-cs.CL | 2024-08-28 |
| 41 | Can Visual Language Models Replace OCR-Based Visual Question Answering Pipelines in Production? A Case Study in Retail Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our study includes two commercial models, GPT-4V [16] and GPT-4o [17], as well as four open-source models: InternVL [5], LLaVA 1.5 [12], LLaVA-NeXT [13], and CogAgent [9]. |
Bianca Lamm; Janis Keuper; | arxiv-cs.CV | 2024-08-28 |
| 42 | Evidence-Enhanced Triplet Generation Framework for Hallucination Alleviation in Generative Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the hallucination in generative question answering (GQA) where the answer can not be derived from the document, we propose a novel evidence-enhanced triplet generation framework, EATQA, encouraging the model to predict all the combinations of (Question, Evidence, Answer) triplet by flipping the source pair and the target label to understand their logical relationships, i.e., predict Answer(A), Question(Q), and Evidence(E) given a QE, EA, and QA pairs, respectively. |
Haowei Du; Huishuai Zhang; Dongyan Zhao; | arxiv-cs.CL | 2024-08-27 |
| 43 | Grounded Multi-Hop VideoQA in Long-Form Egocentric Videos Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop an automated pipeline to create multi-hop question-answering pairs with associated temporal evidence, enabling to construct a large-scale dataset for instruction-tuning. |
Qirui Chen; Shangzhe Di; Weidi Xie; | arxiv-cs.CV | 2024-08-26 |
| 44 | Question Answering System of Bridge Design Specification Based on Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Through the self-built question and answer task dataset, based on the tensorflow and keras deep learning platform framework, the model is constructed and trained to predict the start position and end position of the answer in the bridge design specification given by the user. |
Leye Zhang; Xiangxiang Tian; Hongjun Zhang; | arxiv-cs.CL | 2024-08-25 |
| 45 | IQA-EVAL: Automatic Evaluation of Human-Model Interactive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce an automatic evaluation framework IQA-EVAL to Interactive Question Answering Evaluation. |
Ruosen Li; Barry Wang; Ruochen Li; Xinya Du; | arxiv-cs.CL | 2024-08-24 |
| 46 | Internal and External Knowledge Interactive Refinement Framework for Knowledge-Intensive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new internal and external knowledge interactive refinement paradigm dubbed IEKR to utilize internal knowledge in LLM to help retrieve relevant knowledge from the external knowledge base, as well as exploit the external knowledge to refine the hallucination of generated internal knowledge. |
Haowei Du; Dongyan Zhao; | arxiv-cs.CL | 2024-08-23 |
| 47 | Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this report, we introduce Vintern-1B, a reliable 1-billion-parameters multimodal large language model (MLLM) for Vietnamese language tasks. |
KHANG T. DOAN et. al. | arxiv-cs.LG | 2024-08-22 |
| 48 | Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Model Based on Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Commercial companies face the dual challenges of privacy protection and resource constraints when involving LLMs for fine-tuning. This paper propose a novel framework, Self-Evolution, designed to address these issues by leveraging lightweight open-source LLMs through multiple iterative fine-tuning rounds. |
SHENGLIN ZHANG et. al. | arxiv-cs.AI | 2024-08-22 |
| 49 | Assessing Modality Bias in Video Question Answering Benchmarks with Multimodal Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing video question-answering (VidQA) benchmarks and datasets often exhibit a bias toward a single modality, despite the goal of requiring advanced reasoning skills that integrate diverse modalities to answer the queries. In this work, we introduce the modality importance score (MIS) to identify such bias. |
JEAN PARK et. al. | arxiv-cs.LG | 2024-08-22 |
| 50 | RConE: Rough Cone Embedding for Multi-Hop Logical Query Answering on Multi-Modal Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose RConE, an embedding method to capture the multi-modal information needed to answer a query. |
Mayank Kharbanda; Rajiv Ratn Shah; Raghava Mutharaju; | arxiv-cs.AI | 2024-08-21 |
| 51 | Multimodal Datasets and Benchmarks for Reasoning About Dynamic Spatio-Temporality in Everyday Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We used a 3D simulator to create artificial video data with standardized annotations, aiming to aid in the development of Embodied AI. |
Takanori Ugai; Kensho Hara; Shusaku Egami; Ken Fukuda; | arxiv-cs.AI | 2024-08-21 |
| 52 | What Are The Limits of Cross-lingual Dense Passage Retrieval for Low-resource Languages? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we analyze the capabilities of the multi-lingual Dense Passage Retriever (mDPR) for extremely low-resource languages. |
Jie Wu; Zhaochun Ren; Suzan Verberne; | arxiv-cs.IR | 2024-08-21 |
| 53 | DyGKT: Dynamic Graph Learning for Knowledge Tracing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The three dynamical characteristics above contain the great potential to revolutionize the existing knowledge tracing methods. Along this line, we propose a Dynamic Graph-based Knowledge Tracing model, namely DyGKT. |
KE CHENG et. al. | kdd | 2024-08-21 |
| 54 | Mathematical Information Retrieval: Search and Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The framework is used to organize and relate the other core topics of the book, including interactions between people and systems, representing math formulas in sources, and evaluation. |
Richard Zanibbi; Behrooz Mansouri; Anurag Agarwal; | arxiv-cs.IR | 2024-08-21 |
| 55 | DocTabQA: Answering Questions from Long Documents Using Tables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the QTabA dataset, encompassing 300 financial documents, accompanied by manually annotated 1.5k question-table pairs. |
Haochen Wang; Kai Hu; Haoyu Dong; Liangcai Gao; | arxiv-cs.CL | 2024-08-21 |
| 56 | Differentiating Choices Via Commonality for Multiple-Choice Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel MCQA model by differentiating choices through identifying and eliminating their commonality, called DCQA. |
WENQING DENG et. al. | arxiv-cs.CL | 2024-08-21 |
| 57 | FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the emergence of various open source methods and web-enhanced commercial systems such as Bing Chat, two critical problems remain unsolved, i.e., the lack of factuality and clear logic in the generated long-form answers. In this paper, we remedy these issues via a systematic study on answer generation in web-enhanced LFQA. |
TIANCHI CAI et. al. | kdd | 2024-08-21 |
| 58 | 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. | acl | 2024-08-20 |
| 59 | TaPERA: Enhancing Faithfulness and Interpretability in Long-Form Table QA By Content Planning and Execution-based Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While Large language models based systems have made significant progress, it often hallucinates, especially when the task involves complex reasoning over tables. To tackle this issue, we propose a new LLM-based framework, TaPERA, for LFTQA tasks. |
Yilun Zhao; Lyuhao Chen; Arman Cohan; Chen Zhao; | acl | 2024-08-20 |
| 60 | 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. | acl | 2024-08-20 |
| 61 | EWEK-QA : Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Second, web-retrieved contents are usually obtained by some simple heuristics such as fixed length or breakpoints which might lead to splitting information into pieces. To mitigate these issues, we propose our enhanced web and efficient knowledge graph (KG) retrieval solution (EWEK-QA) to enrich the content of the extracted knowledge fed to the system. |
MOHAMMAD DEHGHAN et. al. | acl | 2024-08-20 |
| 62 | Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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; | acl | 2024-08-20 |
| 63 | Transferable and Efficient Non-Factual Content Detection Via Probe Training with Offline Consistency Checking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes PiNose, which trains a probing model on offline self-consistency checking results, thereby circumventing the need for human-annotated data and achieving transferability across diverse data distributions. |
XIAOKANG ZHANG et. al. | acl | 2024-08-20 |
| 64 | Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the performance of this retrieve-then-read paradigm is constrained by the retriever and the inevitable noise in the retrieved documents. To mitigate these challenges, we introduce a novel generate-then-ground (GenGround) framework, synergizing the parametric knowledge of LLMs and external documents to solve a multi-hop question. |
ZHENGLIANG SHI et. al. | acl | 2024-08-20 |
| 65 | Learning Relational Decomposition of Queries for Question Answering from Tables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By learning to imitate a restricted subset of SQL-like algebraic operations, we demonstrate that their execution flow provides intermediate supervision steps that allow for increased generalization and structural reasoning compared to classical approaches. |
Rapha�l Mouravieff; Benjamin Piwowarski; Sylvain Lamprier; | acl | 2024-08-20 |
| 66 | Domain Adaptation for Subjective Induction Questions Answering on Products By Adversarial Disentangled Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It is hard for traditional methods to work well without considering the shift of domain patterns. To address this problem, we propose a novel domain-adaptive model. |
YUFENG ZHANG et. al. | acl | 2024-08-20 |
| 67 | 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. | acl | 2024-08-20 |
| 68 | 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. | acl | 2024-08-20 |
| 69 | Is Table Retrieval A Solved Problem? Exploring Join-Aware Multi-Table Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: If the join plan is not considered in the retrieval stage, the subsequent steps of reasoning and answering based on those retrieved tables are likely to be incorrect. To address this problem, we introduce a method that uncovers useful join relations for any query and database during table retrieval. |
Peter Baile Chen; Yi Zhang; Dan Roth; | acl | 2024-08-20 |
| 70 | ColBERT Retrieval and Ensemble Response Scoring for Language Model Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The Specializing Large Language Models for Telecom Networks challenge aimed to enhance the performance of two small language models, Phi-2 and Falcon-7B in telecommunication question answering. In this paper, we present our question answering systems for this challenge. |
Alex Gichamba; Tewodros Kederalah Idris; Brian Ebiyau; Eric Nyberg; Teruko Mitamura; | arxiv-cs.CL | 2024-08-20 |
| 71 | Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce Tree-of-Traversals, a novel zero-shot reasoning algorithm that enables augmentation of black-box LLMs with one or more KGs. |
ELAN MARKOWITZ et. al. | acl | 2024-08-20 |
| 72 | Multilingual Non-Factoid Question Answering with Silver Answers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the scope of such datasets for low-resource languages remains limited, with only a few works centered on factoid-based QuADs and none on non-factoid QuADs. Therefore, this work presents MuNfQuAD, a multilingual QuAD with non-factoid questions. |
Ritwik Mishra; Sreeram Vennam; Rajiv Ratn Shah; Ponnurangam Kumaraguru; | arxiv-cs.CL | 2024-08-20 |
| 73 | HOLMES: Hyper-Relational Knowledge Graphs for Multi-hop Question Answering Using LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this simplistic approach is query-agnostic and the extracted facts are ambiguous as they lack context. To address these drawbacks and to enable LLMs to answer complex (multi-hop) questions with ease, we propose to use a knowledge graph (KG) that is context-aware and is distilled to contain query-relevant information. |
Pranoy Panda; Ankush Agarwal; Chaitanya Devaguptapu; Manohar Kaul; Prathosh Ap; | acl | 2024-08-20 |
| 74 | To Generate or to Retrieve? On The Effectiveness of Artificial Contexts for Medical Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents MedGENIE, the first generate-then-read framework for multiple-choice question answering in medicine. |
Giacomo Frisoni; Alessio Cocchieri; Alex Presepi; Gianluca Moro; Zaiqiao Meng; | acl | 2024-08-20 |
| 75 | MMToM-QA: Multimodal Theory of Mind Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: People can flexibly reason about another person�s mind based on conceptual representations (e. g. , goals, beliefs, plans) extracted from any available data. To address this, we introduce a multimodal Theory of Mind question answering (MMToM-QA) benchmark. |
CHUANYANG JIN et. al. | acl | 2024-08-20 |
| 76 | ProtT3: Protein-to-Text Generation for Text-based Protein Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address their limitations, we introduce ProtT3, a framework for Protein-to-Text Generation for Text-based Protein Understanding. |
ZHIYUAN LIU et. al. | acl | 2024-08-20 |
| 77 | RetinaQA: A Robust Knowledge Base Question Answering Model for Both Answerable and Unanswerable Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recent research has found that such models, when superficially adapted to detect answerability, struggle to satisfactorily identify the different categories of unanswerable questions, and simultaneously preserve good performance for answerable questions. Towards addressing this issue, we propose RetinaQA, a new KBQA model that unifies two key ideas in a single KBQA architecture: (a) discrimination over candidate logical forms, rather than generating these, for handling schema-related unanswerability, and (b) sketch-filling-based construction of candidate logical forms for handling data-related unaswerability. |
Prayushi Faldu; Indrajit Bhattacharya; Mausam .; | acl | 2024-08-20 |
| 78 | SymKGQA: Few-Shot Knowledge Graph Question Answering Via Symbolic Program Generation and Execution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recently, a new LF called KoPL has been introduced that explicitly models complex reasoning process step-by-step in a symbolic manner and has shown SOTA on KQA Pro in fully-supervised setting. Inspired by this, we propose SymKGQA framework that generates step-by-step Symbolic LF i. e. , KoPL in a few-shot in-context learning setting using LLM. |
Prerna Agarwal; Nishant Kumar; Srikanta Bedathur; | acl | 2024-08-20 |
| 79 | AutoAct: Automatic Agent Learning from Scratch for QA Via Self-Planning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we introduce AutoAct, an automatic agent learning framework for QA that does not rely on large-scale annotated data and synthetic planning trajectories from closed-source models (e. g. , GPT-4). |
SHUOFEI QIAO et. al. | acl | 2024-08-20 |
| 80 | Paraphrasing in Affirmative Terms Improves Negation Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we experiment with seamless strategies that incorporate affirmative interpretations (i. e. , paraphrases without negation) to make models more robust against negation. |
MohammadHossein Rezaei; Eduardo Blanco; | acl | 2024-08-20 |
| 81 | CoDi: Conversational Distillation for Grounded Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Secondly, high-quality conversational datasets are often scarce, small, and domain-specific. Addressing these challenges, we introduce a novel data distillation framework named CoDi (short for Conversational Distillation, pronounced Cody), allowing us to synthesize large-scale, assistant-style datasets in a steerable and diverse manner. |
PATRICK HUBER et. al. | arxiv-cs.CL | 2024-08-20 |
| 82 | SyllabusQA: A Course Logistics Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce SyllabusQA, an open-source dataset with 63 real course syllabi covering 36 majors, containing 5,078 open-ended course logistics-related question-answer pairs that are diverse in both question types and answer formats. |
Nigel Fernandez; Alexander Scarlatos; Andrew Lan; | acl | 2024-08-20 |
| 83 | 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; | acl | 2024-08-20 |
| 84 | Never Lost in The Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional Training Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 Position-Agnostic Multi-step QA (PAM QA). |
JUNQING HE et. al. | acl | 2024-08-20 |
| 85 | Spiral of Silence: 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 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. | acl | 2024-08-20 |
| 86 | FastFiD: Improve Inference Efficiency of Open Domain Question Answering Via Sentence Selection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Nevertheless, this framework can be relatively time-consuming, particularly due to the extensive length of the gathered passages. To address this, we introduce FastFiD in this paper, a novel approach that executes sentence selection on the encoded passages. |
Yufei Huang; Xu Han; Maosong Sun; | acl | 2024-08-20 |
| 87 | 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. |
MICHAEL KRUMDICK et. al. | acl | 2024-08-20 |
| 88 | SEER: Facilitating Structured Reasoning and Explanation Via Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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. | acl | 2024-08-20 |
| 89 | 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. | acl | 2024-08-20 |
| 90 | 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; | acl | 2024-08-20 |
| 91 | 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: 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; | acl | 2024-08-20 |
| 92 | FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark 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; | acl | 2024-08-20 |
| 93 | Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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; | acl | 2024-08-20 |
| 94 | SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The paper introduces SceMQA, a novel benchmark for scientific multimodal question answering at the college entrance level. |
ZHENWEN LIANG et. al. | acl | 2024-08-20 |
| 95 | Safety Alignment in NLP Tasks: Weakly Aligned Summarization As An In-Context Attack Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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; | acl | 2024-08-20 |
| 96 | 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. | acl | 2024-08-20 |
| 97 | Temporal Knowledge Question Answering Via Abstract Reasoning Induction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we address the challenge of enhancing temporal knowledge reasoning in Large Language Models (LLMs). |
Ziyang Chen; Dongfang Li; Xiang Zhao; Baotian Hu; Min Zhang; | acl | 2024-08-20 |
| 98 | BeamAggR: Beam Aggregation Reasoning Over Multi-source Knowledge for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, significant challenges still persist, including inaccurate and insufficient retrieval for complex questions, as well as difficulty in integrating multi-source knowledge. To address this, we propose Beam Aggregation Reasoning (BeamAggR), a reasoning framework for knowledge-intensive multi-hop QA. |
ZHENG CHU et. al. | acl | 2024-08-20 |
| 99 | 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 and compare our model�s performance against it, demonstrating an improvement in results, particularly in later turns of the conversation, when dealing with questions that include a large historical context. |
Hamed Hemati; Hamid Beigy; | acl | 2024-08-20 |
| 100 | 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. | acl | 2024-08-20 |
| 101 | Putting People in LLMs’ Shoes: Generating Better Answers Via Question Rewriter Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, their effectiveness in QA is often undermined by the vagueness of user questions. To address this issue, we introduce single-round instance-level prompt optimization, referred to as question rewriter. |
Junhao Chen; Bowen Wang; Zhouqiang jiang; Yuta Nakashima; | arxiv-cs.CL | 2024-08-20 |
| 102 | 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 Related Code 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 et. al. | acl | 2024-08-20 |
| 103 | SOTOPIA-p: Interactive Learning of Socially Intelligent Language Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This social learning process is largely understudied by existing research on building language agents. Motivated by this gap, we propose an interactive learning method, SOTOPIA-p, that improves the social intelligence of language agents. |
RUIYI WANG et. al. | acl | 2024-08-20 |
| 104 | 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 clarifying the similarity criterion. |
LETIAN PENG et. al. | acl | 2024-08-20 |
| 105 | Ranking Generated Answers: On The Agreement of Retrieval Models with Humans on Consumer Health Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a method for evaluating LLM answers that uses ranking signals as a substitute for explicit relevance judgements. |
Sebastian Heineking; Jonas Probst; Daniel Steinbach; Martin Potthast; Harrisen Scells; | arxiv-cs.IR | 2024-08-19 |
| 106 | TableBench: A Comprehensive and Complex Benchmark for Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite these achievements, LLMs still encounter significant challenges when applied in industrial scenarios, particularly due to the increased complexity of reasoning required with real-world tabular data, underscoring a notable disparity between academic benchmarks and practical applications. To address this discrepancy, we conduct a detailed investigation into the application of tabular data in industrial scenarios and propose a comprehensive and complex benchmark TableBench, including 18 fields within four major categories of table question answering (TableQA) capabilities. |
XIANJIE WU et. al. | arxiv-cs.CL | 2024-08-17 |
| 107 | Developing A Llama-Based Chatbot for CI/CD Question Answering: A Case Study at Ericsson Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents our experience developing a Llama-based chatbot for question answering about continuous integration and continuous delivery (CI/CD) at Ericsson, a multinational telecommunications company. |
Daksh Chaudhary; Sri Lakshmi Vadlamani; Dimple Thomas; Shiva Nejati; Mehrdad Sabetzadeh; | arxiv-cs.SE | 2024-08-17 |
| 108 | RealMedQA: A Pilot Biomedical Question Answering Dataset Containing Realistic Clinical Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present RealMedQA, a dataset of realistic clinical questions generated by humans and an LLM. |
GREGORY KELL et. al. | arxiv-cs.CL | 2024-08-16 |
| 109 | Beyond The Hype: A Dispassionate Look at Vision-language Models in Medical Scenario Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we introduce RadVUQA, a novel Radiological Visual Understanding and Question Answering benchmark, to comprehensively evaluate existing LVLMs. |
Yang Nan; Huichi Zhou; Xiaodan Xing; Guang Yang; | arxiv-cs.CV | 2024-08-16 |
| 110 | MuRAR: A Simple and Effective Multimodal Retrieval and Answer Refinement Framework for Multimodal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a simple and effective framework named MuRAR (Multimodal Retrieval and Answer Refinement). |
ZHENGYUAN ZHU et. al. | arxiv-cs.IR | 2024-08-16 |
| 111 | LLaVA-Surg: Towards Multimodal Surgical Assistant Via Structured Surgical Video Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: One major contributing factor is the absence of datasets in the surgical field. In this paper, we create a new dataset, Surg-QA, consisting of 102,000 surgical video-instruction pairs, the largest of its kind so far. |
JIAJIE LI et. al. | arxiv-cs.CV | 2024-08-15 |
| 112 | Assessing and Enhancing Large Language Models in Rare Disease Question-answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a rare disease question-answering (ReDis-QA) dataset to evaluate the performance of LLMs in diagnosing rare diseases. |
GUANCHU WANG et. al. | arxiv-cs.CE | 2024-08-15 |
| 113 | W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose W-RAG by utilizing the ranking capabilities of LLMs to create weakly labeled data for training dense retrievers. |
Jinming Nian; Zhiyuan Peng; Qifan Wang; Yi Fang; | arxiv-cs.CL | 2024-08-15 |
| 114 | IIU: Independent Inference Units for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Independent Inference Units (IIU) for fine-grained multi-modal reasoning to decompose intra-modal information by the functionally independent units. |
Yili Li; Jing Yu; Keke Gai; Gang Xiong; | arxiv-cs.CV | 2024-08-15 |
| 115 | QirK: Question Answering Via Intermediate Representation on Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We demonstrate QirK, a system for answering natural language questions on Knowledge Graphs (KG). |
JAN LUCA SCHEERER et. al. | arxiv-cs.DB | 2024-08-14 |
| 116 | Enhancing Visual Question Answering Through Ranking-Based Hybrid Training and Multimodal Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Current VQA models struggle with complex questions due to limitations in capturing and integrating multimodal information effectively. To address these challenges, we propose the Rank VQA model, which leverages a ranking-inspired hybrid training strategy to enhance VQA performance. |
Peiyuan Chen; Zecheng Zhang; Yiping Dong; Li Zhou; Han Wang; | arxiv-cs.CV | 2024-08-14 |
| 117 | Fine-tuning Large Language Models with Human-inspired Learning Strategies in Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we extend previous research by evaluating both curriculum-based and non-curriculum-based learning strategies across multiple LLMs, using human-defined and automated data labels for medical question answering. |
Yushi Yang; Andrew M. Bean; Robert McCraith; Adam Mahdi; | arxiv-cs.CL | 2024-08-14 |
| 118 | A RAG-Based Question-Answering Solution for Cyber-Attack Investigation and Attribution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the constantly evolving field of cybersecurity, it is imperative for analysts to stay abreast of the latest attack trends and pertinent information that aids in the investigation and attribution of cyber-attacks. In this work, we introduce the first question-answering (QA) model and its application that provides information to the cybersecurity experts about cyber-attacks investigations and attribution. |
Sampath Rajapaksha; Ruby Rani; Erisa Karafili; | arxiv-cs.CR | 2024-08-12 |
| 119 | Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing approaches struggle with CQA due to two main challenges: (1) precisely identifying conditions and their logical relationship, and (2) verifying and solving the conditions. To address these challenges, we propose Chain of Condition, a novel prompting approach by firstly identifying all conditions and constructing their logical relationships explicitly according to the document, then verifying whether these conditions are satisfied, finally solving the logical expression by tools to indicate any missing conditions and generating the answer based on the resolved conditions. |
Jiuheng Lin; Yuxuan Lai; Yansong Feng; | arxiv-cs.CL | 2024-08-10 |
| 120 | Sportify: Question Answering with Embedded Visualizations and Personified Narratives for Sports Video Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This complexity leads to a need for additional information and explanation, which can distract fans from the game. To tackle these challenges, we present Sportify, a Visual Question Answering system that integrates narratives and embedded visualization for demystifying basketball tactical questions, aiding fans in understanding various game aspects. |
Chunggi Lee; Tica Lin; Hanspeter Pfister; Chen Zhu-Tian; | arxiv-cs.HC | 2024-08-09 |
| 121 | Surgical-VQLA++: Adversarial Contrastive Learning for Calibrated Robust Visual Question-Localized Answering in Robotic Surgery Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the inability of VQA models to visually indicate the regions of interest corresponding to the given questions results in incomplete comprehension of the surgical scene. To tackle this, we propose the surgical visual question localized-answering (VQLA) for precise and context-aware responses to specific queries regarding surgical images. |
LONG BAI et. al. | arxiv-cs.CV | 2024-08-09 |
| 122 | Towards A Generative Approach for Emotion Detection and Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: But can they perform emotional reasoning by concatenating `Let’s think step-by-step’ to the input prompt? In this paper we investigate this question along with introducing a novel approach to zero-shot emotion detection and emotional reasoning using LLMs. |
Ankita Bhaumik; Tomek Strzalkowski; | arxiv-cs.CL | 2024-08-09 |
| 123 | VideoQA in The Era of LLMs: An Empirical Study Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work conducts a timely and comprehensive study of Video-LLMs’ behavior in VideoQA, aiming to elucidate their success and failure modes, and provide insights towards more human-like video understanding and question answering. |
JUNBIN XIAO et. al. | arxiv-cs.CV | 2024-08-08 |
| 124 | Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, RALMs still struggle with unanswerable queries, where the retrieved contexts do not contain the correct answer, and with conflicting information, where different sources provide contradictory answers due to imperfect retrieval. This study introduces an in-context learning-based approach to enhance the reasoning capabilities of RALMs, making them more robust in imperfect retrieval scenarios. |
Seong-Il Park; Seung-Woo Choi; Na-Hyun Kim; Jay-Yoon Lee; | arxiv-cs.CL | 2024-08-08 |
| 125 | EfficientRAG: Efficient Retriever for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce EfficientRAG, an efficient retriever for multi-hop question answering. |
ZIYUAN ZHUANG et. al. | arxiv-cs.CL | 2024-08-08 |
| 126 | Enhancing Healthcare Through Large Language Models: A Study on Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a detailed study of various LLMs trained on the MedQuAD medical question-answering dataset, with a focus on identifying the most effective model for providing accurate medical information. |
Haoran Yu; Chang Yu; Zihan Wang; Dongxian Zou; Hao Qin; | arxiv-cs.CL | 2024-08-07 |
| 127 | Targeted Visual Prompting for Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this, region-based questions have been proposed as a means to assess and enhance actual visual understanding through compositional evaluation. To combine these two perspectives, this paper introduces targeted visual prompting to equip MLLMs with region-based questioning capabilities. |
Sergio Tascon-Morales; Pablo Márquez-Neila; Raphael Sznitman; | arxiv-cs.CV | 2024-08-06 |
| 128 | Entity Retrieval for Answering Entity-Centric Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose Entity Retrieval, a novel retrieval method which rather than relying on question-document similarity, depends on the salient entities within the question to identify the retrieval documents. |
Hassan S. Shavarani; Anoop Sarkar; | arxiv-cs.IR | 2024-08-05 |
| 129 | XMainframe: A Large Language Model for Mainframe Modernization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we introduce XMainframe, a state-of-the-art large language model (LLM) specifically designed with knowledge of mainframe legacy systems and COBOL codebases. |
ANH T. V. DAU et. al. | arxiv-cs.CL | 2024-08-05 |
| 130 | Leveraging Inter-Chunk Interactions for Enhanced Retrieval in Large Language Model-Based Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous research typically handles paragraphs from external documents in isolation, resulting in a lack of context and ambiguous references, particularly in multi-document and complex tasks. To overcome these challenges, we propose a new retrieval framework IIER, that leverages Inter-chunk Interactions to Enhance Retrieval. |
TIEZHENG GUO et. al. | arxiv-cs.CL | 2024-08-05 |
| 131 | Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We executed this pipeline and introduced the PUGG dataset, the first Polish KBQA dataset, and novel datasets for MRC and IR. |
ALBERT SAWCZYN et. al. | arxiv-cs.AI | 2024-08-05 |
| 132 | RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces RAGEval, a framework for automatically generating evaluation datasets to evaluate the knowledge usage ability of different LLMs in different scenarios. |
KUNLUN ZHU et. al. | arxiv-cs.CL | 2024-08-02 |
| 133 | BioRAG: A RAG-LLM Framework for Biological Question Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The question-answering system for Life science research, which is characterized by the rapid pace of discovery, evolving insights, and complex interactions among knowledge entities, presents unique challenges in maintaining a comprehensive knowledge warehouse and accurate information retrieval. To address these issues, we introduce BioRAG, a novel Retrieval-Augmented Generation (RAG) with the Large Language Models (LLMs) framework. |
CHENGRUI WANG et. al. | arxiv-cs.CL | 2024-08-02 |
| 134 | Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: When using large language models (LLMs) in knowledge-intensive tasks, such as open-domain question answering, external context can bridge a gap between external knowledge and LLM’s parametric knowledge. |
YOUNA KIM et. al. | arxiv-cs.CL | 2024-08-02 |
| 135 | DebateQA: Evaluating Question Answering on Debatable Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, traditional QA benchmarks assume fixed answers are inadequate for this purpose. To address this, we introduce DebateQA, a dataset of 2,941 debatable questions, each accompanied by multiple human-annotated partial answers that capture a variety of perspectives. |
Rongwu Xu; Xuan Qi; Zehan Qi; Wei Xu; Zhijiang Guo; | arxiv-cs.CL | 2024-08-02 |
| 136 | Towards Flexible Evaluation for Generative Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although Visual Question Answering (VQA) could serve as a developed test field, limitations of VQA evaluation, like the inflexible pattern of Exact Match, have hindered MLLMs from demonstrating their real capability and discourage rich responses. Therefore, this paper proposes the use of semantics-based evaluators for assessing unconstrained open-ended responses on VQA datasets. |
Huishan Ji; Qingyi Si; Zheng Lin; Weiping Wang; | arxiv-cs.CV | 2024-08-01 |
| 137 | Prompting Medical Large Vision-Language Models to Diagnose Pathologies By Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose two prompting strategies for MLVLMs that reduce hallucination and improve VQA performance. |
Danfeng Guo; Demetri Terzopoulos; | arxiv-cs.CV | 2024-07-31 |
| 138 | Decomposed Prompting to Answer Questions on A Course Discussion Board Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose and evaluate a question-answering system that uses decomposed prompting to classify and answer student questions on a course discussion board. |
BRANDON JAIPERSAUD et. al. | arxiv-cs.CL | 2024-07-30 |
| 139 | Boosting Audio Visual Question Answering Via Key Semantic-Aware Cues Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a Temporal-Spatial Perception Model (TSPM), which aims to empower the model to perceive key visual and auditory cues related to the questions. |
Guangyao Li; Henghui Du; Di Hu; | arxiv-cs.CV | 2024-07-30 |
| 140 | Advancing Vietnamese Visual Question Answering with Transformer and Convolutional Integration Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite the prevalence of approaches in English, there is a notable lack of systems specifically developed for certain languages, particularly Vietnamese. This study aims to bridge this gap by conducting comprehensive experiments on the Vietnamese Visual Question Answering (ViVQA) dataset, demonstrating the effectiveness of our proposed model. |
Ngoc Son Nguyen; Van Son Nguyen; Tung Le; | arxiv-cs.CV | 2024-07-30 |
| 141 | SimpleLLM4AD: An End-to-End Vision-Language Model with Graph Visual Question Answering for Autonomous Driving Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, by utilizing vision-language model (VLM), we proposed an e2eAD method called SimpleLLM4AD. |
Peiru Zheng; Yun Zhao; Zhan Gong; Hong Zhu; Shaohua Wu; | arxiv-cs.CV | 2024-07-30 |
| 142 | Pyramid Coder: Hierarchical Code Generator for Compositional Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there are challenges in enabling LLMs to comprehend the usage of image processing modules and generate relevant code. To overcome these challenges, this paper introduces PyramidCoder, a novel prompting framework for PVQA models. |
Ruoyue Shen; Nakamasa Inoue; Koichi Shinoda; | arxiv-cs.CV | 2024-07-30 |
| 143 | Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To fill the gap, we propose a visualization-referenced instruction tuning approach to guide the training dataset enhancement and model development. |
Xingchen Zeng; Haichuan Lin; Yilin Ye; Wei Zeng; | arxiv-cs.CV | 2024-07-29 |
| 144 | AdaCoder: Adaptive Prompt Compression for Programmatic Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they often require long input prompts to provide the LLM with sufficient API usage details to generate relevant code. To address this limitation, we propose AdaCoder, an adaptive prompt compression framework for VPMs. |
Mahiro Ukai; Shuhei Kurita; Atsushi Hashimoto; Yoshitaka Ushiku; Nakamasa Inoue; | arxiv-cs.AI | 2024-07-28 |
| 145 | Answerability Fields: Answerable Location Estimation Via Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Answerability Fields, a novel approach to predicting answerability within complex indoor environments. |
Daichi Azuma; Taiki Miyanishi; Shuhei Kurita; Koya Sakamoto; Motoaki Kawanabe; | arxiv-cs.CV | 2024-07-26 |
| 146 | A Role-specific Guided Large Language Model for Ophthalmic Consultation Based on Stylistic Differentiation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose EyeDoctor, an ophthalmic medical questioning large language model that enhances accuracy through doctor-patient role perception guided and an augmented knowledge base with external disease information. |
LAIYI FU et. al. | arxiv-cs.CL | 2024-07-25 |
| 147 | Constructing The CORD-19 Vaccine Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce new dataset ‘CORD-19-Vaccination’ to cater to scientists specifically looking into COVID-19 vaccine-related research. |
Manisha Singh; Divy Sharma; Alonso Ma; Bridget Tyree; Margaret Mitchell; | arxiv-cs.CL | 2024-07-25 |
| 148 | Audio Entailment: Assessing Deductive Reasoning for Audio Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce the novel task of Audio Entailment to evaluate an ALM’s deductive reasoning ability. |
SOHAM DESHMUKH et. al. | arxiv-cs.SD | 2024-07-25 |
| 149 | The Geometry of Queries: Query-Based Innovations in Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce Query-Based Retrieval Augmented Generation (QB-RAG), a novel approach that pre-computes a database of potential queries from a content base using LLMs. |
Eric Yang; Jonathan Amar; Jong Ha Lee; Bhawesh Kumar; Yugang Jia; | arxiv-cs.LG | 2024-07-25 |
| 150 | 3D Question Answering for City Scene Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: From the method perspective, we propose a Scene graph enhanced City-level Understanding method (Sg-CityU), which utilizes the scene graph to introduce the spatial semantic. |
PENGLEI SUN et. al. | arxiv-cs.CV | 2024-07-24 |
| 151 | ScholarChemQA: Unveiling The Power of Language Models in Chemical Research Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Correspondingly, we introduce a QAMatch model, specifically designed to effectively answer chemical questions by fully leveraging our collected data. |
XIUYING CHEN et. al. | arxiv-cs.CL | 2024-07-23 |
| 152 | Shared Imagination: LLMs Hallucinate Alike Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel setting, imaginary question answering (IQA), to better understand model similarity. |
Yilun Zhou; Caiming Xiong; Silvio Savarese; Chien-Sheng Wu; | arxiv-cs.CL | 2024-07-23 |
| 153 | Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While previous works have focused on understanding the recollection abilities of LLMs or their ability to identify imponderable/unanswerable questions, we believe there is a need for an effective AA evaluation method. Therefore, we propose a black-box evaluation methodology to examine and understand the AA of LLMs across a variety of multiple-choice QA tasks. |
Nishanth Madhusudhan; Sathwik Tejaswi Madhusudhan; Vikas Yadav; Masoud Hashemi; | arxiv-cs.CL | 2024-07-23 |
| 154 | KaPQA: Knowledge-Augmented Product Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, accurately assessing the performance of these applications remains a challenge, mainly due to the lack of suitable benchmarks that effectively simulate real-world scenarios. To address this challenge, we introduce two product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop products to help evaluate the performance of existing models on domain-specific product QA tasks. |
SWETHA EPPALAPALLY et. al. | arxiv-cs.CL | 2024-07-22 |
| 155 | Exploring The Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we conduct an extensive empirical study on representation learning for downstream Visual Question Answering (VQA), which requires an accurate compositional understanding of the scene. |
Amir Mohammad Karimi Mamaghan; Samuele Papa; Karl Henrik Johansson; Stefan Bauer; Andrea Dittadi; | arxiv-cs.CV | 2024-07-22 |
| 156 | MMInstruct: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive Diversity Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To construct MMInstruct, we propose an instruction generation data engine that leverages GPT-4V, GPT-3.5, and manual correction. |
YANGZHOU LIU et. al. | arxiv-cs.CV | 2024-07-22 |
| 157 | LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Albeit the progress, few public benchmark is available to measure such development. To mitigate this gap, we introduce LongVideoBench, a question-answering benchmark that features video-language interleaved inputs up to an hour long. |
Haoning Wu; Dongxu Li; Bei Chen; Junnan Li; | arxiv-cs.CV | 2024-07-22 |
| 158 | RadioRAG: Factual Large Language Models for Enhanced Diagnostics in Radiology Using Dynamic Retrieval Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Large language models (LLMs) have advanced the field of artificial intelligence (AI) in medicine. |
SOROOSH TAYEBI ARASTEH et. al. | arxiv-cs.CL | 2024-07-22 |
| 159 | OMoS-QA: A Dataset for Cross-Lingual Extractive Question Answering in A German Migration Context Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we present OMoS-QA, a dataset of German and English questions paired with relevant trustworthy documents and manually annotated answers, specifically tailored to this scenario. |
Steffen Kleinle; Jakob Prange; Annemarie Friedrich; | arxiv-cs.CL | 2024-07-22 |
| 160 | End-to-End Video Question Answering with Frame Scoring Mechanisms and Adaptive Sampling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Simply uniformly sampling frames or indiscriminately aggregating frame-level visual features often falls short in capturing the nuanced and relevant contexts of videos to well perform VideoQA. To mitigate these issues, we propose VidF4, a novel VideoQA framework equipped with tailored frame selection strategy for effective and efficient VideoQA. |
JIANXIN LIANG et. al. | arxiv-cs.CV | 2024-07-21 |
| 161 | Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Off-the-shelf RAG flows are well pretrained on general-purpose documents, yet they encounter significant challenges when being applied to knowledge-intensive vertical domains, such as electronic design automation (EDA). This paper addresses such issue by proposing a customized RAG framework along with three domain-specific techniques for EDA tool documentation QA, including a contrastive learning scheme for text embedding model fine-tuning, a reranker distilled from proprietary LLM, and a generative LLM fine-tuned with high-quality domain corpus. |
Yuan Pu; Zhuolun He; Tairu Qiu; Haoyuan Wu; Bei Yu; | arxiv-cs.CL | 2024-07-21 |
| 162 | Knowledge Acquisition Disentanglement for Knowledge-based Visual Question Answering with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, the “forward-only” answering process fails to explicitly capture the knowledge needs of LLMs, which can further hurt answering quality. To cope with the above limitations, we propose DKA: Disentangled Knowledge Acquisition from LLM feedback, a training-free framework that disentangles knowledge acquisition to avoid confusion and uses LLM’s feedback to specify the required knowledge. |
WENBIN AN et. al. | arxiv-cs.CV | 2024-07-21 |
| 163 | Evaluating Language Models As Risk Scores Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we focus on the use of language models as risk scores for unrealizable prediction tasks. |
André F. Cruz; Moritz Hardt; Celestine Mendler-Dünner; | arxiv-cs.LG | 2024-07-19 |
| 164 | RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, most existing datasets for this task are either constructed using a single source corpus or consist of short extractive answers, which fall short of evaluating large language model (LLM) based RAG-QA systems on cross-domain generalization. To address these limitations, we create Long-form RobustQA (LFRQA), a new dataset comprising human-written long-form answers that integrate short extractive answers from multiple documents into a single, coherent narrative, covering 26K queries and large corpora across seven different domains. |
RUJUN HAN et. al. | arxiv-cs.CL | 2024-07-18 |
| 165 | INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering Capability of LLMs for Indic Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the evaluation of LLMs’ capabilities in non-English languages for context-based QA is limited by the scarcity of benchmarks in non-English languages. To address this gap, we introduce Indic-QA, the largest publicly available context-grounded question-answering dataset for 11 major Indian languages from two language families. |
Abhishek Kumar Singh; Rudra Murthy; Vishwajeet kumar; Jaydeep Sen; Ganesh Ramakrishnan; | arxiv-cs.LG | 2024-07-18 |
| 166 | Visual Haystacks: Answering Harder Questions About Sets of Images Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a new public benchmark, dubbed Visual Haystacks (VHs), specifically designed to evaluate LMMs’ capabilities in visual retrieval and reasoning over sets of unrelated images, where we perform comprehensive evaluations demonstrating that even robust closed-source models struggle significantly. Towards addressing these shortcomings, we introduce MIRAGE (Multi-Image Retrieval Augmented Generation), a novel retrieval/QA framework tailored for LMMs that confronts the challenges of MIQA with marked efficiency and accuracy improvements over baseline methods. |
TSUNG-HAN WU et. al. | arxiv-cs.CV | 2024-07-18 |
| 167 | Clinical Reading Comprehension with Encoder-Decoder Models Enhanced By Direct Preference Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we combine encoder-decoder models with the direct preference optimization (DPO) method to improve over prior state of the art for the RadQA radiology question answering task by 12-15 F1 points. |
Md Sultan Al Nahian; Ramakanth Kavuluru; | arxiv-cs.IR | 2024-07-18 |
| 168 | Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with An Iterative Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel iterative RAG method called ReSP, equipped with a dual-function summarizer. |
Zhouyu Jiang; Mengshu Sun; Lei Liang; Zhiqiang Zhang; | arxiv-cs.CL | 2024-07-17 |
| 169 | Continual Learning for Temporal-Sensitive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we explore an emerging research area of Continual Learning for Temporal Sensitive Question Answering (CLTSQA). |
WANQI YANG et. al. | arxiv-cs.CL | 2024-07-17 |
| 170 | Search Engines, LLMs or Both? Evaluating Information Seeking Strategies for Answering Health Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we focus on their merits in answering health questions. |
Marcos Fernández-Pichel; Juan C. Pichel; David E. Losada; | arxiv-cs.IR | 2024-07-17 |
| 171 | EchoSight: Advancing Visual-Language Models with Wiki Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce EchoSight, a novel multimodal Retrieval-Augmented Generation (RAG) framework that enables large language models (LLMs) to answer visual questions requiring fine-grained encyclopedic knowledge. |
Yibin Yan; Weidi Xie; | arxiv-cs.CV | 2024-07-17 |
| 172 | TurkishMMLU: Measuring Massive Multitask Language Understanding in Turkish Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce the first multitask, multiple-choice Turkish QA benchmark, TurkishMMLU, to evaluate LLMs’ understanding of the Turkish language. |
Arda Yüksel; Abdullatif Köksal; Lütfi Kerem Şenel; Anna Korhonen; Hinrich Schütze; | arxiv-cs.CL | 2024-07-17 |
| 173 | Multimodal Reranking for Knowledge-Intensive Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce an additional module, a multi-modal reranker, to improve the ranking quality of knowledge candidates for answer generation. |
Haoyang Wen; Honglei Zhuang; Hamed Zamani; Alexander Hauptmann; Michael Bendersky; | arxiv-cs.CL | 2024-07-16 |
| 174 | Reasoning with Large Language Models, A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We provide an in-depth coverage of core approaches and open problems, and we propose a research agenda for the near future. |
ASKE PLAAT et. al. | arxiv-cs.AI | 2024-07-16 |
| 175 | TM-PATHVQA:90000+ Textless Multilingual Questions for Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this work implements a speech-based VQA system by introducing a Textless Multilingual Pathological VQA (TMPathVQA) dataset, an expansion of the PathVQA dataset, containing spoken questions in English, German & French. |
Tonmoy Rajkhowa; Amartya Roy Chowdhury; Sankalp Nagaonkar; Achyut Mani Tripathi; | arxiv-cs.CV | 2024-07-16 |
| 176 | Video-Language Alignment Via Spatio-Temporal Graph Transformer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel Spatio-Temporal Graph Transformer module to uniformly learn spatial and temporal contexts for video-language alignment pre-training (dubbed STGT). |
SHI-XUE ZHANG et. al. | arxiv-cs.CV | 2024-07-16 |
| 177 | Fine-grained Hallucination Detection and Mitigation in Long-form Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work introduces HaluQuestQA, the first hallucination dataset with localized error annotations for human-written and model-generated LFQA answers. |
Rachneet Sachdeva; Yixiao Song; Mohit Iyyer; Iryna Gurevych; | arxiv-cs.CL | 2024-07-16 |
| 178 | Unraveling The Truth: Do LLMs Really Understand Charts? A Deep Dive Into Consistency and Robustness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We investigate two key aspects: 1) the models’ ability to handle varying levels of chart and question complexity, and 2) their robustness across different visual representations of the same underlying data. |
SRIJA MUKHOPADHYAY et. al. | arxiv-cs.CL | 2024-07-15 |
| 179 | Graphusion: Leveraging Large Language Models for Scientific Knowledge Graph Fusion and Construction in NLP Education Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce Graphusion, a zero-shot KGC framework from free text. |
RUI YANG et. al. | arxiv-cs.CL | 2024-07-15 |
| 180 | GenSco: Can Question Decomposition Based Passage Alignment Improve Question Answering? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate whether providing aligned context via a carefully selected passage sequence leads to better answer generation by the LLM for multi-hop QA. |
Barah Fazili; Koustava Goswami; Natwar Modani; Inderjeet Nair; | arxiv-cs.CL | 2024-07-14 |
| 181 | NativQA: Multilingual Culturally-Aligned Natural Query for LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a scalable framework, NativQA, to seamlessly construct culturally and regionally aligned QA datasets in native languages, for LLM evaluation and tuning. |
MD. ARID HASAN et. al. | arxiv-cs.CL | 2024-07-13 |
| 182 | SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing question-answering (QA) datasets based on scientific papers are limited in scale and focus solely on textual content. To address this limitation, we introduce SPIQA (Scientific Paper Image Question Answering), the first large-scale QA dataset specifically designed to interpret complex figures and tables within the context of scientific research articles across various domains of computer science. |
Shraman Pramanick; Rama Chellappa; Subhashini Venugopalan; | arxiv-cs.CL | 2024-07-12 |
| 183 | One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel and comprehensive solution to enhance both the robustness and efficiency of question answering (QA) systems through supervised contrastive learning (SCL). |
Bo Wang; Tsunenori Mine; | arxiv-cs.CL | 2024-07-12 |
| 184 | CompAct: Compressing Retrieved Documents Actively for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Context compression tackles this issue by filtering out irrelevant information, but current methods still struggle in realistic scenarios where crucial information cannot be captured with a single-step approach. To overcome this limitation, we introduce CompAct, a novel framework that employs an active strategy to condense extensive documents without losing key information. |
Chanwoong Yoon; Taewhoo Lee; Hyeon Hwang; Minbyul Jeong; Jaewoo Kang; | arxiv-cs.CL | 2024-07-12 |
| 185 | PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces PersonaRAG, a novel framework incorporating user-centric agents to adapt retrieval and generation based on real-time user data and interactions. |
Saber Zerhoudi; Michael Granitzer; | arxiv-cs.IR | 2024-07-12 |
| 186 | Bridging The Gap Between Information Seeking and Product Search Systems: Q&A Recommendation for E-commerce Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The recent success of Large Language Models (LLMs) has opened up an opportunity to bridge the gap between the two tasks to help customers achieve their goals quickly and effectively by integrating conversational QA within product search. In this paper, we propose to recommend users Question-Answer (Q&A) pairs that are relevant to their product search and can help them make a purchase decision. |
Saar Kuzi; Shervin Malmasi; | arxiv-cs.CL | 2024-07-12 |
| 187 | Segmentation-guided Attention for Visual Question Answering from Remote Sensing Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose to embed an attention mechanism guided by segmentation into a RSVQA pipeline. |
LUCREZIA TOSATO et. al. | arxiv-cs.CV | 2024-07-11 |
| 188 | Uncertainty Estimation of Large Language Models in Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we benchmark popular UE methods with different model sizes on medical question-answering datasets. |
Jiaxin Wu; Yizhou Yu; Hong-Yu Zhou; | arxiv-cs.CL | 2024-07-11 |
| 189 | AutoBencher: Creating Salient, Novel, Difficult Datasets for Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present three desiderata for a good benchmark for language models: (i) salience (e.g., knowledge about World War II is more salient than a random day in history), (ii) novelty (i.e., the benchmark reveals new trends in model rankings not shown by previous benchmarks), and (iii) difficulty (i.e., the benchmark should be difficult for existing models, leaving headroom for future improvement). |
Xiang Lisa Li; Evan Zheran Liu; Percy Liang; Tatsunori Hashimoto; | arxiv-cs.CL | 2024-07-11 |
| 190 | RAG Vs. Long Context: Examining Frontier Large Language Models for Environmental Review Document Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Large Language Models (LLMs) have been applied to many research problems across various domains. |
HUNG PHAN et. al. | arxiv-cs.CL | 2024-07-09 |
| 191 | WSI-VQA: Interpreting Whole Slide Images By Generative Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel framework (WSI-VQA) to interpret WSIs by generative visual question answering. |
Pingyi Chen; Chenglu Zhu; Sunyi Zheng; Honglin Li; Lin Yang; | arxiv-cs.CV | 2024-07-08 |
| 192 | MST5 — Multilingual Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this research, we propose a simplified approach to enhance multilingual KGQA systems by incorporating linguistic context and entity information directly into the processing pipeline of a language model. |
NIKIT SRIVASTAVA et. al. | arxiv-cs.CL | 2024-07-08 |
| 193 | Question Answering with Texts and Tables Through Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel architecture to generate multi-hop answers to open domain questions that require information from texts and tables, using the Open Table-and-Text Question Answering dataset for validation and training. |
MARCOS M. JOSÉ et. al. | arxiv-cs.CL | 2024-07-05 |
| 194 | Sponsored Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present the first formal analysis of a sponsored QA platform. |
Tommy Mordo; Moshe Tennenholtz; Oren Kurland; | arxiv-cs.GT | 2024-07-05 |
| 195 | Second Place Solution of WSDM2023 Toloka Visual Question Answering Challenge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present our solution for the WSDM2023 Toloka Visual Question Answering Challenge. |
Xiangyu Wu; Zhouyang Chi; Yang Yang; Jianfeng Lu; | arxiv-cs.CV | 2024-07-05 |
| 196 | On Scalable Oversight with Weak LLMs Judging Strong LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we study debate, where two AI’s compete to convince a judge; consultancy, where a single AI tries to convince a judge that asks questions; and compare to a baseline of direct question-answering, where the judge just answers outright without the AI. |
ZACHARY KENTON et. al. | arxiv-cs.LG | 2024-07-05 |
| 197 | From Data to Commonsense Reasoning: The Use of Large Language Models for Explainable AI Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the effectiveness of large language models (LLMs) on different QA tasks with a focus on their abilities in reasoning and explainability. |
Stefanie Krause; Frieder Stolzenburg; | arxiv-cs.AI | 2024-07-04 |
| 198 | Leveraging Topic Specificity and Social Relationships for Expert Finding in Community Question Answering Platforms Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present TUEF, a Topic-oriented User-Interaction model for Expert Finding, which aims to fully and transparently leverage the heterogeneous information available within online question-answering communities. |
Maddalena Amendola; Andrea Passarella; Raffaele Perego; | arxiv-cs.IR | 2024-07-04 |
| 199 | STOC-TOT: Stochastic Tree-of-Thought with Constrained Decoding for Complex Reasoning in Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose STOC-TOT, a stochastic tree-of-thought reasoning prompting method with constrained decoding for MHQA and conduct a detailed comparison with other reasoning prompts on different question types and reasoning types. |
Zhenyu Bi; Daniel Hajialigol; Zhongkai Sun; Jie Hao; Xuan Wang; | arxiv-cs.CL | 2024-07-04 |
| 200 | Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a robust discriminator named RelD to effectively detect hallucination in LLMs’ generated answers. |
YUYAN CHEN et. al. | arxiv-cs.CL | 2024-07-04 |
| 201 | FSM: A Finite State Machine Based Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a prompting method, Finite State Machine (FSM) to enhance the reasoning capabilities of LLM for complex tasks in addition to improved effectiveness and trustworthiness. |
XIAOCHEN WANG et. al. | arxiv-cs.CL | 2024-07-03 |
| 202 | UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs’ Memorization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces UnSeenTimeQA, a novel time-sensitive question-answering (TSQA) benchmark that diverges from traditional TSQA benchmarks by avoiding factual and web-searchable queries. |
MD NAYEM UDDIN et. al. | arxiv-cs.CL | 2024-07-03 |
| 203 | Align and Aggregate: Compositional Reasoning with Video Alignment and Answer Aggregation for Video Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the recent progress made in Video Question-Answering (VideoQA), these methods typically function as black-boxes, making it difficult to understand their reasoning processes and perform consistent compositional reasoning. To address these challenges, we propose a \textit{model-agnostic} Video Alignment and Answer Aggregation (VA$^{3}$) framework, which is capable of enhancing both compositional consistency and accuracy of existing VidQA methods by integrating video aligner and answer aggregator modules. |
Zhaohe Liao; Jiangtong Li; Li Niu; Liqing Zhang; | arxiv-cs.CV | 2024-07-03 |
| 204 | VDMA: Video Question Answering with Dynamically Generated Multi-Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Video Question Answering with Dynamically Generated Multi-Agents (VDMA). |
Noriyuki Kugo; Tatsuya Ishibashi; Kosuke Ono; Yuji Sato; | arxiv-cs.CV | 2024-07-03 |
| 205 | DSAMR: Dual-Stream Attention Multi-hop Reasoning for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
YANHAN SUN et. al. | Expert Syst. Appl. | 2024-07-01 |
| 206 | Calibrated Large Language Models for Binary Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel approach that utilizes the inductive Venn–Abers predictor (IVAP) to calibrate the probabilities associated with the output tokens corresponding to the binary labels. |
Patrizio Giovannotti; Alexander Gammerman; | arxiv-cs.CL | 2024-07-01 |
| 207 | The Solution for The ICCV 2023 Perception Test Challenge 2023 — Task 6 — Grounded VideoQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a grounded video question-answering solution. |
Hailiang Zhang; Dian Chao; Zhihao Guan; Yang Yang; | arxiv-cs.CV | 2024-07-01 |
| 208 | Event-centric Hierarchical Hyperbolic Graph for Multi-hop Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View |
Xun Zhu; Wang Gao; Tianyu Li; Wenguang Yao; Hongtao Deng; | Eng. Appl. Artif. Intell. | 2024-07-01 |
| 209 | Eliminating Position Bias of Language Models: A Mechanistic Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Further, our empirical study on object detection reveals that position bias is also present in vision-language models (VLMs). Based on the above analyses, we propose to ELIMINATE position bias caused by different input segment orders (e.g., options in LM-as-a-judge, retrieved documents in QA) in a TRAINING-FREE ZERO-SHOT manner. |
ZIQI WANG et. al. | arxiv-cs.CL | 2024-07-01 |
| 210 | Dynamic Few-Shot Learning for Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we introduce a novel approach called Dynamic Few-Shot Learning (DFSL). |
Jacopo D’Abramo; Andrea Zugarini; Paolo Torroni; | arxiv-cs.CL | 2024-07-01 |
| 211 | M2QA: Multi-domain Multilingual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This prevents the transfer of NLP systems from well-resourced languages and domains to non-dominant language-domain combinations. To address this gap, we introduce M2QA, a multi-domain multilingual question answering benchmark. |
LEON ENGLÄNDER et. al. | arxiv-cs.CL | 2024-07-01 |
| 212 | Hierarchical Memory for Long Video QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper describes our champion solution to the LOVEU Challenge @ CVPR’24, Track 1 (Long Video VQA). |
YIQIN WANG et. al. | arxiv-cs.CV | 2024-06-30 |
| 213 | BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Science Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: On the widely used popular knowledge graph, we discover over 90 factual errors which provide scenarios for agents to make discoveries and demonstrate the effectiveness of our approach. |
XINNA LIN et. al. | arxiv-cs.CL | 2024-06-29 |
| 214 | Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature Distillation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing approaches at the intersection of Continual Learning and Visual Question Answering (VQA) do not study how the multimodal nature of the input affects the learning dynamics of a model. In this paper, we demonstrate that each modality evolves at different rates across a continuum of tasks and that this behavior occurs in established encoder-only models as well as modern recipes for developing Vision & Language (VL) models. |
Malvina Nikandrou; Georgios Pantazopoulos; Ioannis Konstas; Alessandro Suglia; | arxiv-cs.CV | 2024-06-27 |
| 215 | Follow-Up Questions Improve Documents Generated By Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study investigates the impact of Large Language Models (LLMs) generating follow-up questions in response to user requests for short (1-page) text documents. |
Bernadette J Tix; | arxiv-cs.CL | 2024-06-27 |
| 216 | TrustUQA: A Trustful Framework for Unified Structured Data Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose UnifiedTQA, a trustful QA framework that can simultaneously support multiple types of structured data in a unified way. |
WEN ZHANG et. al. | arxiv-cs.CL | 2024-06-27 |
| 217 | Context Matters: An Empirical Study of The Impact of Contextual Information in Temporal Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce two new context-rich TQA datasets, ContextAQA and ContextTQE, and provide comprehensive evaluations and guidelines for training robust TQA models. |
DAN SCHUMACHER et. al. | arxiv-cs.CL | 2024-06-27 |
| 218 | FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce FlowVQA, a novel benchmark aimed at assessing the capabilities of visual question-answering multimodal language models in reasoning with flowcharts as visual contexts. |
SHUBHANKAR SINGH et. al. | arxiv-cs.CL | 2024-06-27 |
| 219 | The Illusion of Competence: Evaluating The Effect of Explanations on Users’ Mental Models of Visual Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our goal is to determine whether participants can perceive the limitations of the system. |
JUDITH SIEKER et. al. | arxiv-cs.CL | 2024-06-27 |
| 220 | Disentangling Knowledge-based and Visual Reasoning By Question Decomposition in KB-VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the Knowledge-Based visual question-answering problem, for which given a question, the models need to ground it into the visual modality to find the answer. |
Elham J. Barezi; Parisa Kordjamshidi; | arxiv-cs.AI | 2024-06-26 |
| 221 | Advancing Question Answering on Handwritten Documents: A State-of-the-Art Recognition-Based Model for HW-SQuAD Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel recognition-based approach that improves upon the previous state-of-the-art on the HW-SQuAD and BenthamQA datasets. |
Aniket Pal; Ajoy Mondal; C. V. Jawahar; | arxiv-cs.CV | 2024-06-25 |
| 222 | Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Prompts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we empirically investigate \emph{visual fairness} in several mainstream LVLMs and audit their performance disparities across sensitive demographic attributes, based on public fairness benchmark datasets (e.g., FACET). |
Xuyang Wu; Yuan Wang; Hsin-Tai Wu; Zhiqiang Tao; Yi Fang; | arxiv-cs.CL | 2024-06-25 |
| 223 | CaLMQA: Exploring Culturally Specific Long-form Question Answering Across 23 Languages Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While LFQA has been well-studied in English, this research has not been extended to other languages. To bridge this gap, we introduce CaLMQA, a collection of 1.5K complex culturally specific questions spanning 23 languages and 51 culturally agnostic questions translated from English into 22 other languages. |
SHANE ARORA et. al. | arxiv-cs.CL | 2024-06-25 |
| 224 | Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing benchmarks employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-context applications. To bridge this gap, we propose a novel long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA). |
MINZHENG WANG et. al. | arxiv-cs.CL | 2024-06-25 |
| 225 | Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present explicit diversity conditions for QAG, focusing on spatial aspects, question types, and entities, substantially increasing diversity in QA generation. |
Vikas Yadav; Hyuk Joon Kwon; Vijay Srinivasan; Hongxia Jin; | arxiv-cs.CL | 2024-06-25 |
| 226 | Context-augmented Retrieval: A Novel Framework for Fast Information Retrieval Based Response Generation Using Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For the same, this work proposes a new approach Context Augmented retrieval (CAR), where partitioning of vector database by real-time classification of information flowing into the corpus is done. |
Sai Ganesh; Anupam Purwar; Gautam B; | arxiv-cs.IR | 2024-06-24 |
| 227 | DEXTER: A Benchmark for Open-domain Complex Question Answering Using LLMs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While retrieval performance for classical QA tasks is well explored, their capabilities for heterogeneous complex retrieval tasks, especially in an open-domain setting, and the impact on downstream QA performance, are relatively unexplored. To address this, in this work, we propose a benchmark composing diverse complex QA tasks and provide a toolkit to evaluate state-of-the-art pre-trained dense and sparse retrieval models in an open-domain setting. |
Venktesh V. Deepali Prabhu; Avishek Anand; | arxiv-cs.CL | 2024-06-24 |
| 228 | CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a collaborative augmentation framework, CogMG, leveraging knowledge graphs to address the limitations of LLMs in QA scenarios, explicitly targeting the problems of incomplete knowledge coverage and knowledge update misalignment. |
Tong Zhou; Yubo Chen; Kang Liu; Jun Zhao; | arxiv-cs.CL | 2024-06-24 |
| 229 | HCQA @ Ego4D EgoSchema Challenge 2024 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this report, we present our champion solution for Ego4D EgoSchema Challenge in CVPR 2024. |
HAOYU ZHANG et. al. | arxiv-cs.CV | 2024-06-22 |
| 230 | Tri-VQA: Triangular Reasoning Medical Visual Question Answering for Multi-Attribute Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the construction of a more cohesive and stable Med-VQA structure. |
Lin Fan; Xun Gong; Cenyang Zheng; Yafei Ou; | arxiv-cs.LG | 2024-06-21 |
| 231 | Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the performance of this retrieve-then-read paradigm is constrained by the retriever and the inevitable noise in the retrieved documents. To mitigate these challenges, we introduce a novel generate-then-ground (GenGround) framework, synergizing the parametric knowledge of LLMs and external documents to solve a multi-hop question. |
ZHENGLIANG SHI et. al. | arxiv-cs.CL | 2024-06-21 |
| 232 | 70B-parameter Large Language Models in Japanese Medical Question-answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here we utilize multiple 70B-parameter LLMs for the first time and show that instruction tuning using Japanese medical question-answering dataset significantly improves the ability of Japanese LLMs to solve Japanese medical license exams, surpassing 50\% in accuracy. |
Issey Sukeda; Risa Kishikawa; Satoshi Kodera; | arxiv-cs.CL | 2024-06-21 |
| 233 | Does Object Grounding Really Reduce Hallucination of Large Vision-Language Models? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, in contrast, we offer the first systematic analysis of the effect of fine-grained object grounding on LVLM hallucination under an evaluation protocol that more realistically captures LVLM hallucination in open generation. |
Gregor Geigle; Radu Timofte; Goran Glavaš; | arxiv-cs.CV | 2024-06-20 |
| 234 | 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 *TRAQ*, which provides the first end-to-end statistical correctness guarantee for RAG. |
Shuo Li; Sangdon Park; Insup Lee; Osbert Bastani; | naacl | 2024-06-20 |
| 235 | LLaSA: Large Multimodal Agent for Human Activity Analysis Through Wearable Sensors Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce SensorCaps, a dataset of 26,288 IMU-derived activity narrations, and OpenSQA, an instruction-following dataset with 257,562 question-answer pairs. |
Sheikh Asif Imran; Mohammad Nur Hossain Khan; Subrata Biswas; Bashima Islam; | arxiv-cs.CL | 2024-06-20 |
| 236 | 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. | naacl | 2024-06-20 |
| 237 | Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models Through Question Complexity IF:3 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 Park; | naacl | 2024-06-20 |
| 238 | TTQA-RS- A Break-down Prompting Approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we have proposed a model – TTQA-RS: A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization. |
Jayetri Bardhan; Bushi Xiao; Daisy Zhe Wang; | arxiv-cs.CL | 2024-06-20 |
| 239 | Is Prompt Transfer Always Effective? An Empirical Study of Prompt Transfer for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we characterize the question answering task based on features such as answer format and empirically investigate the transferability of soft prompts for the first time. |
Minji Jung; Soyeon Park; Jeewoo Sul; Yong Suk Choi; | naacl | 2024-06-20 |
| 240 | 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; | naacl | 2024-06-20 |
| 241 | A Learn-Then-Reason Model Towards Generalization in Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: At the core of KBLLaMA, we study (1) how to organize new knowledge about KBQA and (2) how to facilitate the learning of the organized knowledge. |
Lingxi Zhang; Jing Zhang; Yanling Wang; Cuiping Li; Hong Chen; | arxiv-cs.CL | 2024-06-20 |
| 242 | Evaluating RAG-Fusion with RAGElo: An Automated Elo-based Framework Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This results in difficulties in evaluating RAG variations, like RAG-Fusion (RAGF), in the context of a product QA task at Infineon Technologies. To solve these problems, we propose a comprehensive evaluation framework, which leverages Large Language Models (LLMs) to generate large datasets of synthetic queries based on real user queries and in-domain documents, uses LLM-as-a-judge to rate retrieved documents and answers, evaluates the quality of answers, and ranks different variants of Retrieval-Augmented Generation (RAG) agents with RAGElo’s automated Elo-based competition. |
Zackary Rackauckas; Arthur Câmara; Jakub Zavrel; | arxiv-cs.IR | 2024-06-20 |
| 243 | 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. | naacl | 2024-06-20 |
| 244 | Self-Prompting Large Language Models for Zero-Shot Open-Domain QA IF:3 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; | naacl | 2024-06-20 |
| 245 | Towards Improved Multi-Source Attribution for Long-Form Answer Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite gaining increasing popularity for usage in QA systems and search engines, current LLMs struggle with attribution for long-form responses which require reasoning over multiple evidence sources. To address this, in this paper we aim to improve the attribution capability of LLMs for long-form answer generation to multiple sources, with multiple citations per sentence. |
Nilay Patel; Shivashankar Subramanian; Siddhant Garg; Pratyay Banerjee; Amita Misra; | naacl | 2024-06-20 |
| 246 | Enhancing Contextual Understanding in Large Language Models Through Contrastive Decoding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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; | naacl | 2024-06-20 |
| 247 | 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 MA et. al. | naacl | 2024-06-20 |
| 248 | Robust Few-shot Transfer Learning for Knowledge Base Question Answering with Unanswerable Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Real-world KBQA applications require models that are (1) robust — e.g., can differentiate between answerable and unanswerable questions, and (2) low-resource — do not require large training data. Towards this goal, we propose the novel task of few-shot transfer for KBQA with unanswerable questions. |
Riya Sawhney; Indrajit Bhattacharya; | arxiv-cs.CL | 2024-06-20 |
| 249 | 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. |
Sindhu Kishore; Hangfeng He; | naacl | 2024-06-20 |
| 250 | PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models As Decision Makers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we conduct a study to utilize LLMs as a solution for decision making that requires complex data analysis. |
Myeonghwa Lee; Seonho An; Min-Soo Kim; | naacl | 2024-06-20 |
| 251 | 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 Glava�; Anna Korhonen; Ivan Vulic; | naacl | 2024-06-20 |
| 252 | 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; Liu Yong; Shen Huang; | naacl | 2024-06-20 |
| 253 | SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This means that producing novel models and measuring the performance of multilingual LLMs in low-resource languages is challenging. To mitigate this, we propose $\textbf{S}$yn$\textbf{DAR}$in, a method for generating and validating QA datasets for low-resource languages. |
Gayane Ghazaryan; Erik Arakelyan; Pasquale Minervini; Isabelle Augenstein; | arxiv-cs.CL | 2024-06-20 |
| 254 | Temporal Knowledge Graph Question Answering: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work aims to serve as a comprehensive reference for TKGQA and to stimulate further research. |
MIAO SU et. al. | arxiv-cs.CL | 2024-06-20 |
| 255 | FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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; | naacl | 2024-06-20 |
| 256 | 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; | naacl | 2024-06-20 |
| 257 | Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a novel framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs). |
JUNJIE WANG et. al. | arxiv-cs.CL | 2024-06-20 |
| 258 | Augmenting Query and Passage for Retrieval-Augmented Generation Using LLMs for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple yet efficient method called question and passage augmentation via LLMs for open-domain QA. |
Minsang Kim; Cheoneum Park; Seungjun Baek; | arxiv-cs.CL | 2024-06-20 |
| 259 | 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. | naacl | 2024-06-20 |
| 260 | CPopQA: Ranking Cultural Concept Popularity By LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the extent to which an LLM effectively captures corpus-level statistical trends of concepts for reasoning, especially long-tail ones, is largely 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), particularly focusing on these concepts� popularity in the United States and the United Kingdom, respectively. |
Ming Jiang; Mansi Joshi; | naacl | 2024-06-20 |
| 261 | On Narrative Question Answering Skills Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing task-level skill views oversimplify the multidimensional nature of tasks, while question-level taxonomies face issues in evaluation and methodology. To address these challenges, we introduce a more inclusive skill taxonomy that synthesizes and redefines narrative understanding skills from previous taxonomies and includes a generation skill dimension from the answering perspective. |
Emil Kalbaliyev; Kairit Sirts; | naacl | 2024-06-20 |
| 262 | Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce Thread, a novel data organization paradigm that transforms documents into logic units based on their inter-connectivity. |
KAIKAI AN et. al. | arxiv-cs.AI | 2024-06-19 |
| 263 | Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present MIRAGE –Model Internals-based RAG Explanations — a plug-and-play approach using model internals for faithful answer attribution in RAG applications. |
Jirui Qi; Gabriele Sarti; Raquel Fernández; Arianna Bisazza; | arxiv-cs.CL | 2024-06-19 |
| 264 | Towards Robust Evaluation: A Comprehensive Taxonomy of Datasets and Metrics for Open Domain Question Answering in The Era of Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel taxonomy for ODQA datasets that incorporates both the modality and difficulty of the question types. |
Akchay Srivastava; Atif Memon; | arxiv-cs.CL | 2024-06-19 |
| 265 | AlanaVLM: A Multimodal Embodied AI Foundation Model for Egocentric Video Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, current Vision-Language Models (VLMs) primarily focus on third-person view videos, neglecting the richness of egocentric perceptual experience. To address this gap, we propose three key contributions. First, we introduce the Egocentric Video Understanding Dataset (EVUD) for training VLMs on video captioning and question answering tasks specific to egocentric videos. |
ALESSANDRO SUGLIA et. al. | arxiv-cs.CV | 2024-06-19 |
| 266 | QRMeM: Unleash The Length Limitation Through Question Then Reflection Memory Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing techniques face challenges with static knowledge integration, leading to insufficient adaptation to task-specific needs and missing multi-segmentation relationships, which hinders the dynamic reorganization and logical combination of relevant segments during the response process. To address these issues, we introduce a novel strategy, Question then Reflection Memory Mechanism (QRMeM), incorporating a dual-structured memory pool. |
BO WANG et. al. | arxiv-cs.CL | 2024-06-18 |
| 267 | Problem-Solving in Language Model Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To improve the reasoning and question-answering capabilities of Large Language Models (LLMs), several multi-agent approaches have been introduced. |
Ciaran Regan; Alexandre Gournail; Mizuki Oka; | arxiv-cs.AI | 2024-06-18 |
| 268 | On The Robustness of Language Models for Tabular Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We highlight the need for improved methodologies, including structure-aware self-attention mechanisms and better handling of domain-specific tabular data, to develop more reliable LLMs for table comprehension. |
Kushal Raj Bhandari; Sixue Xing; Soham Dan; Jianxi Gao; | arxiv-cs.CL | 2024-06-18 |
| 269 | Rationale-based Ensemble of Multiple QA Strategies for Zero-shot Knowledge-based VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Current zero-shot K-VQA methods usually translate an image to a single type of textual decision context and use a text-based model to answer the question based on it, which conflicts with the fact that K-VQA questions often require the combination of multiple question-answering strategies. In light of this, we propose Rationale-based Ensemble of Answer Context Tactics (REACT) to achieve a dynamic ensemble of multiple question-answering tactics, comprising Answer Candidate Generation (ACG) and Rationale-based Strategy Fusion (RSF). |
Miaoyu Li; Haoxin Li; Zilin Du; Boyang Li; | arxiv-cs.CL | 2024-06-18 |
| 270 | From RAGs to Rich Parameters: Probing How Language Models Utilize External Knowledge Over Parametric Information for Factual Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we mechanistically examine the RAG pipeline to highlight that language models take shortcut and have a strong bias towards utilizing only the context information to answer the question, while relying minimally on their parametric memory. |
HITESH WADHWA et. al. | arxiv-cs.CL | 2024-06-18 |
| 271 | Learnable In-Context Vector for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose \textbf{Learnable ICV} (L-ICV) to distill essential task information from demonstrations, improving ICL performance in LMMs. |
YINGZHE PENG et. al. | arxiv-cs.CL | 2024-06-18 |
| 272 | TRACE The Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To enhance the multi-hop reasoning ability of RAG models, we propose TRACE. |
Jinyuan Fang; Zaiqiao Meng; Craig Macdonald; | arxiv-cs.CL | 2024-06-17 |
| 273 | RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To foster sound evaluation of language models, we introduce a new test dataset named RepLiQA, suited for question-answering and topic retrieval tasks. |
JOAO MONTEIRO et. al. | arxiv-cs.CL | 2024-06-17 |
| 274 | FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Food is a rich and varied dimension of cultural heritage, crucial to both individuals and social groups. To bridge the gap in the literature on the often-overlooked regional diversity in this domain, we introduce FoodieQA, a manually curated, fine-grained image-text dataset capturing the intricate features of food cultures across various regions in China. |
WENYAN LI et. al. | arxiv-cs.CL | 2024-06-16 |
| 275 | Central Answer Modeling for An Embodied Multi-LLM System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we consider EQA in a multi-agent framework involving multiple large language models (LLM) based agents independently answering queries about a household environment. |
Bhrij Patel; Vishnu Sashank Dorbala; Amrit Singh Bedi; Dinesh Manocha; | arxiv-cs.LG | 2024-06-16 |
| 276 | HiddenTables & PyQTax: A Cooperative Game and Dataset For TableQA to Ensure Scale and Data Privacy Across A Myriad of Taxonomies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These challenges are engendered from (1) finite context windows for large tables, (2) multi-faceted discrepancies amongst tokenization patterns against cell boundaries, and (3) various limitations stemming from data confidentiality in the process of using external models such as gpt-3.5-turbo. We propose a cooperative game dubbed HiddenTables as a potential resolution to this challenge. |
William Watson; Nicole Cho; Tucker Balch; Manuela Veloso; | arxiv-cs.AI | 2024-06-16 |
| 277 | SHMamba: Structured Hyperbolic State Space Model for Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the self-attention mechanism’s limitations in window modeling and quadratic computational complexity reduce its effectiveness in modeling long sequences. To address these limitations, we propose SHMamba: Structured Hyperbolic State Space Model to integrate the advantages of hyperbolic geometry and state space models. |
Zhe Yang; Wenrui Li; Guanghui Cheng; | arxiv-cs.AI | 2024-06-14 |
| 278 | Beyond Raw Videos: Understanding Edited Videos with Large Multimodal Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we leverage the edited videos on a popular short video platform, \textit{i.e.}, TikTok, and build a video VQA benchmark (named EditVid-QA) covering four typical editing categories, i.e., effect, funny, meme, and game. |
LU XU et. al. | arxiv-cs.CV | 2024-06-14 |
| 279 | EWEK-QA: Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Second, web-retrieved contents are usually obtained by some simple heuristics such as fixed length or breakpoints which might lead to splitting information into pieces. To mitigate these issues, we propose our enhanced web and efficient knowledge graph (KG) retrieval solution (EWEK-QA) to enrich the content of the extracted knowledge fed to the system. |
MOHAMMAD DEHGHAN et. al. | arxiv-cs.CL | 2024-06-14 |
| 280 | Enhancing Question Answering on Charts Through Effective Pre-training Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While the current state-of-the-art approaches for document understanding (both OCR-based and OCR-free) work well, a thorough analysis of their capabilities and limitations has not yet been performed. Therefore, in this work, we addresses the limitation of current VisualQA models when applied to charts and plots. |
ASHIM GUPTA et. al. | arxiv-cs.CL | 2024-06-14 |
| 281 | Datasets for Multilingual Answer Sentence Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce new high-quality datasets for AS2 in five European languages (French, German, Italian, Portuguese, and Spanish), obtained through supervised Automatic Machine Translation (AMT) of existing English AS2 datasets such as ASNQ, WikiQA, and TREC-QA using a Large Language Model (LLM). |
Matteo Gabburo; Stefano Campese; Federico Agostini; Alessandro Moschitti; | arxiv-cs.CL | 2024-06-14 |
| 282 | CHiSafetyBench: A Chinese Hierarchical Safety Benchmark for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce CHiSafetyBench, a dedicated safety benchmark for evaluating LLMs’ capabilities in identifying risky content and refusing answering risky questions in Chinese contexts. |
WENJING ZHANG et. al. | arxiv-cs.CL | 2024-06-14 |
| 283 | Precision Empowers, Excess Distracts: Visual Question Answering With Dynamically Infused Knowledge In Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce an approach for KBVQA, augmenting the existing vision-language transformer encoder-decoder (OFA) model. |
Manas Jhalani; Annervaz K M; Pushpak Bhattacharyya; | arxiv-cs.CL | 2024-06-14 |
| 284 | Language-aware Visual Semantic 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; | cvpr | 2024-06-13 |
| 285 | 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: Motivated by the need for a more comprehensive evaluation we introduce a novel dataset comprising 23781 questions derived from 10124 image-text pairs. |
Kang Chen; Xiangqian Wu; | cvpr | 2024-06-13 |
| 286 | Optimizing Visual Question Answering Models for Driving: Bridging The Gap Between Human and Machine Attention Patterns Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an approach integrating filters to optimize the model’s attention mechanisms, prioritizing relevant objects and improving accuracy. |
Kaavya Rekanar; Martin Hayes; Ganesh Sistu; Ciaran Eising; | arxiv-cs.CV | 2024-06-13 |
| 287 | Causal-CoG: A Causal-Effect Look at Context Generation for Boosting Multi-modal Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While Multi-modal Language Models (MLMs) demon strate impressive multimodal ability they still struggle on providing factual and precise responses for tasks like vi sual question answering (VQA). In this paper we address this challenge from the perspective of contextual informa tion. |
Shitian Zhao; Zhuowan Li; Yadong Lu; Alan Yuille; Yan Wang; | cvpr | 2024-06-13 |
| 288 | 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; | cvpr | 2024-06-13 |
| 289 | 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 neighborhood consistency to identify unreliable responses from a black-box vision-language model in question answering tasks. |
Zaid Khan; Yun Fu; | cvpr | 2024-06-13 |
| 290 | On Scaling Up A Multilingual Vision and Language Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We explore the boundaries of scaling up 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. | cvpr | 2024-06-13 |
| 291 | Towards Multilingual Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we work towards extending Audio-Visual Question Answering (AVQA) to multilingual settings. |
ORCHID CHETIA PHUKAN et. al. | arxiv-cs.LG | 2024-06-13 |
| 292 | 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; | cvpr | 2024-06-13 |
| 293 | 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; | cvpr | 2024-06-13 |
| 294 | 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; | cvpr | 2024-06-13 |
| 295 | Can I Trust Your Answer? Visually Grounded Video Question Answering IF:3 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; | cvpr | 2024-06-13 |
| 296 | OpenEQA: Embodied Question Answering in The Era of Foundation Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a modern formulation of Embodied Question Answering (EQA) as the task of understanding an environment well enough to answer questions about it in natural language. |
ARJUN MAJUMDAR et. al. | cvpr | 2024-06-13 |
| 297 | DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech Units for Spoken Language Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the use of discrete speech units (DSU), rather than continuous-valued speech encoder outputs, that are converted to the LLM token embedding space using the speech adapter. |
SUWON SHON et. al. | arxiv-cs.CL | 2024-06-13 |
| 298 | 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; | cvpr | 2024-06-13 |
| 299 | CoG-DQA: Chain-of-Guiding Learning with Large Language Models for Diagram Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we introduce the Chain-of-Guiding Learning Model for Diagram Question Answering (CoG-DQA) a novel framework that effectively addresses DQA challenges. |
SHAOWEI WANG et. al. | cvpr | 2024-06-13 |
| 300 | DIEM: Decomposition-Integration Enhancing Multimodal Insights Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we propose the Decomposition-Integration Enhancing Multimodal Insight (DIEM) which initially decomposes the given question and image into multiple subquestions and several sub-images aiming to isolate specific elements for more focused analysis. |
XINYI JIANG et. al. | cvpr | 2024-06-13 |
| 301 | 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; | cvpr | 2024-06-13 |
| 302 | Multi-Factor Adaptive Vision Selection for Egocentric Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The challenge of interpreting the world from a human perspective in Artificial Intelligence (AI) is particularly evident in egocentric video question answering, which grapples with issues like small object recognition, noise suppression, and spatial-temporal reasoning. To address these challenges, we introduce the Multi-Factor Adaptive vision Selection (MFAS) framework. |
HAOYU ZHANG et. al. | icml | 2024-06-12 |
| 303 | 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. | icml | 2024-06-12 |
| 304 | 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; Graham Neubig; Daniel Fried; | icml | 2024-06-12 |
| 305 | 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; | icml | 2024-06-12 |
| 306 | Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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; | icml | 2024-06-12 |
| 307 | 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. | icml | 2024-06-12 |
| 308 | DARA: Decomposition-Alignment-Reasoning Autonomous Language Agent for Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To improve the neural-symbolic reasoning capabilities of language agents powered by Large Language Models (LLMs) in KGQA, we propose the DecompositionAlignment-Reasoning Agent (DARA) framework. |
Haishuo Fang; Xiaodan Zhu; Iryna Gurevych; | arxiv-cs.CL | 2024-06-11 |
| 309 | MBBQ: A Dataset for Cross-Lingual Comparison of Stereotypes in Generative LLMs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we present MBBQ (Multilingual Bias Benchmark for Question-answering), a carefully curated version of the English BBQ dataset extended to Dutch, Spanish, and Turkish, which measures stereotypes commonly held across these languages. |
Vera Neplenbroek; Arianna Bisazza; Raquel Fernández; | arxiv-cs.CL | 2024-06-11 |
| 310 | Benchmarking Vision-Language Contrastive Methods for Medical Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Through this study, we aim to answer the following research questions: (i) How transferable are general-domain representations to the medical domain? |
SHUVENDU ROY et. al. | arxiv-cs.CV | 2024-06-11 |
| 311 | Situational Awareness Matters in 3D Vision Language Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Being able to carry out complicated vision language reasoning tasks in 3D space represents a significant milestone in developing household robots and human-centered embodied AI. In this work, we demonstrate that a critical and distinct challenge in 3D vision language reasoning is situational awareness, which incorporates two key components: (1) The autonomous agent grounds its self-location based on a language prompt. |
Yunze Man; Liang-Yan Gui; Yu-Xiong Wang; | arxiv-cs.CV | 2024-06-11 |
| 312 | Scholarly Question Answering Using Large Language Models in The NFDI4DataScience Gateway Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces a scholarly Question Answering (QA) system on top of the NFDI4DataScience Gateway, employing a Retrieval Augmented Generation-based (RAG) approach. |
HAMED BABAEI GIGLOU et. al. | arxiv-cs.CL | 2024-06-11 |
| 313 | VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present the VideoLLaMA 2, a set of Video Large Language Models (Video-LLMs) designed to enhance spatial-temporal modeling and audio understanding in video and audio-oriented tasks. |
ZESEN CHENG et. al. | arxiv-cs.CV | 2024-06-11 |
| 314 | DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To mine the relevance, a two-stage retrieval framework called Dynamic-Relevant Retrieval-Augmented Generation (DR-RAG) is proposed to improve document retrieval recall and the accuracy of answers while maintaining efficiency. |
ZIJIAN HEI et. al. | arxiv-cs.LG | 2024-06-11 |
| 315 | MedExQA: Medical Question Answering Benchmark with Multiple Explanations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces MedExQA, a novel benchmark in medical question-answering, to evaluate large language models’ (LLMs) understanding of medical knowledge through explanations. |
Yunsoo Kim; Jinge Wu; Yusuf Abdulle; Honghan Wu; | arxiv-cs.CL | 2024-06-10 |
| 316 | Evaluating The Retrieval Component in LLM-Based Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study proposes a straightforward baseline for evaluating retrievers in Retrieval-Augmented Generation (RAG)-based chatbots. |
Ashkan Alinejad; Krtin Kumar; Ali Vahdat; | arxiv-cs.CL | 2024-06-10 |
| 317 | HOLMES: Hyper-Relational Knowledge Graphs for Multi-hop Question Answering Using LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this simplistic approach is query-agnostic and the extracted facts are ambiguous as they lack context. To address these drawbacks and to enable LLMs to answer complex (multi-hop) questions with ease, we propose to use a knowledge graph (KG) that is context-aware and is distilled to contain query-relevant information. |
Pranoy Panda; Ankush Agarwal; Chaitanya Devaguptapu; Manohar Kaul; Prathosh A P; | arxiv-cs.CL | 2024-06-10 |
| 318 | Zero-Shot End-To-End Spoken Question Answering In Medical Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our study introduces a novel zero-shot SQA approach, compared to traditional cascade systems. |
Yanis Labrak; Adel Moumen; Richard Dufour; Mickael Rouvier; | arxiv-cs.CL | 2024-06-09 |
| 319 | MrRank: Improving Question Answering Retrieval System Through Multi-Result Ranking Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose an approach that leverages learning-to-rank techniques to combine heterogeneous IR systems. |
Danupat Khamnuansin; Tawunrat Chalothorn; Ekapol Chuangsuwanich; | arxiv-cs.CL | 2024-06-09 |
| 320 | CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: More importantly, although these datasets often extend their linguistic range via translation or some other approaches, they usually keep images the same, resulting in narrow cultural representation. To address these limitations, we construct CVQA, a new Culturally-diverse multilingual Visual Question Answering benchmark, designed to cover a rich set of languages and cultures, where we engage native speakers and cultural experts in the data collection process. |
DAVID ROMERO et. al. | arxiv-cs.CV | 2024-06-09 |
| 321 | MedREQAL: Examining Medical Knowledge Recall of Large Language Models Via Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we examine the capability of LLMs to exhibit medical knowledge recall by constructing a novel dataset derived from systematic reviews — studies synthesizing evidence-based answers for specific medical questions. |
Juraj Vladika; Phillip Schneider; Florian Matthes; | arxiv-cs.CL | 2024-06-09 |
| 322 | RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a weakly supervised method for training the RE simply utilizing question-answer data without any labels for correct contexts. |
Kiseung Kim; Jay-Yoon Lee; | arxiv-cs.CL | 2024-06-09 |
| 323 | Investigating and Addressing Hallucinations of LLMs in Tasks Involving Negation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Negation is important because it adds depth and nuance to the understanding of language and is also crucial for logical reasoning and inference. In this work, we address the above limitation and particularly focus on studying the impact of negation in LLM hallucinations. |
NEERAJ VARSHNEY et. al. | arxiv-cs.CL | 2024-06-08 |
| 324 | Venn Diagram Prompting : Accelerating Comprehension with Scaffolding Effect Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce Venn Diagram (VD) Prompting, an innovative prompting technique which allows Large Language Models (LLMs) to combine and synthesize information across complex, diverse and long-context documents in knowledge-intensive question-answering tasks. |
Sakshi Mahendru; Tejul Pandit; | arxiv-cs.CL | 2024-06-08 |
| 325 | ComplexTempQA: A Large-Scale Dataset for Complex Temporal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce ComplexTempQA,a large-scale dataset consisting of over 100 million question-answer pairs designed to tackle the challenges in temporal question answering. |
Raphael Gruber; Abdelrahman Abdallah; Michael Färber; Adam Jatowt; | arxiv-cs.CL | 2024-06-07 |
| 326 | TCMD: A Traditional Chinese Medicine QA Dataset for Evaluating Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a new medical question-answering (QA) dataset that contains massive manual instruction for solving Traditional Chinese Medicine examination tasks, called TCMD. |
Ping Yu; Kaitao Song; Fengchen He; Ming Chen; Jianfeng Lu; | arxiv-cs.CL | 2024-06-07 |
| 327 | CRAG – Comprehensive RAG Benchmark Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution to alleviate Large Language Model (LLM)’s deficiency in lack of knowledge. Existing RAG datasets, … |
XIAO YANG et. al. | ArXiv | 2024-06-07 |
| 328 | MATTER: Memory-Augmented Transformer Using Heterogeneous Knowledge Sources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce an efficient memory-augmented transformer called MATTER, designed to retrieve relevant knowledge from multiple heterogeneous knowledge sources. |
Dongkyu Lee; Chandana Satya Prakash; Jack FitzGerald; Jens Lehmann; | arxiv-cs.CL | 2024-06-07 |
| 329 | CRAG — Comprehensive RAG Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing RAG datasets, however, do not adequately represent the diverse and dynamic nature of real-world Question Answering (QA) tasks. To bridge this gap, we introduce the Comprehensive RAG Benchmark (CRAG), a factual question answering benchmark of 4,409 question-answer pairs and mock APIs to simulate web and Knowledge Graph (KG) search. |
XIAO YANG et. al. | arxiv-cs.CL | 2024-06-07 |
| 330 | FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced Languages Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While numerous datasets have been developed in English for this purpose, a noticeable void exists in less-resourced languages. To alleviate this gap, our paper introduces machine-translated versions of FairytaleQA, a renowned QA dataset designed to assess and enhance narrative comprehension skills in young children. |
Bernardo Leite; Tomás Freitas Osório; Henrique Lopes Cardoso; | arxiv-cs.CL | 2024-06-06 |
| 331 | Wings: Learning Multimodal LLMs Without Text-only Forgetting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present Wings, a novel MLLM that excels in both text-only dialogues and multimodal comprehension. |
YI-KAI ZHANG et. al. | arxiv-cs.CL | 2024-06-05 |
| 332 | M-QALM: A Benchmark to Assess Clinical Reading Comprehension and Knowledge Recall in Large Language Models Via Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: There is vivid research on adapting Large Language Models (LLMs) to perform a variety of tasks in high-stakes domains such as healthcare. |
ANAND SUBRAMANIAN et. al. | arxiv-cs.CL | 2024-06-05 |
| 333 | Measuring Retrieval Complexity in Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate which questions are challenging for retrieval-based Question Answering (QA). |
Matteo Gabburo; Nicolaas Paul Jedema; Siddhant Garg; Leonardo F. R. Ribeiro; Alessandro Moschitti; | arxiv-cs.CL | 2024-06-05 |
| 334 | UniOQA: A Unified Framework for Knowledge Graph Question Answering with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce UniOQA, a unified framework that integrates two complementary parallel workflows. |
Zhuoyang Li; Liran Deng; Hui Liu; Qiaoqiao Liu; Junzhao Du; | arxiv-cs.CL | 2024-06-04 |
| 335 | Story Generation from Visual Inputs: Techniques, Related Tasks, and Challenges Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Creating engaging narratives from visual data is crucial for automated digital media consumption, assistive technologies, and interactive entertainment. This survey covers methodologies used in the generation of these narratives, focusing on their principles, strengths, and limitations. |
Daniel A. P. Oliveira; Eugénio Ribeiro; David Martins de Matos; | arxiv-cs.CV | 2024-06-04 |
| 336 | I’ve Got The Answer! Interpretation of LLMs Hidden States in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We also identify the layers which have a negative effect on the model’s behavior. As a prospect of practical application of the hypothesis, we propose to train such weak layers additionally in order to improve the quality of the task solution. |
Valeriya Goloviznina; Evgeny Kotelnikov; | arxiv-cs.CL | 2024-06-04 |
| 337 | Translation Deserves Better: Analyzing Translation Artifacts in Cross-lingual Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We find that these artifacts can significantly affect the models, confirmed by extensive experiments across diverse models, languages, and translation processes. In light of this, we present a simple data augmentation strategy that can alleviate the adverse impacts of translation artifacts. |
CHAEHUN PARK et. al. | arxiv-cs.CL | 2024-06-04 |
| 338 | MedFuzz: Exploring The Robustness of Large Language Models in Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we present an adversarial method that we call MedFuzz (for medical fuzzing). |
ROBERT OSAZUWA NESS et. al. | arxiv-cs.CL | 2024-06-03 |
| 339 | Selectively Answering Visual Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Avg BLEU, a calibration score combining the benefits of both sampling and likelihood methods across modalities. |
Julian Martin Eisenschlos; Hernán Maina; Guido Ivetta; Luciana Benotti; | arxiv-cs.CL | 2024-06-03 |
| 340 | Contextualized Sequence Likelihood: Enhanced Confidence Scores for Natural Language Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose enhancing the predicted sequence probability by assigning different weights to various tokens using attention values elicited from the base LLM. |
Zhen Lin; Shubhendu Trivedi; Jimeng Sun; | arxiv-cs.CL | 2024-06-03 |
| 341 | EffiQA: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing approaches that integrate LLMs and KGs either underutilize the reasoning abilities of LLMs or suffer from prohibitive computational costs due to tight coupling. To address these limitations, we propose a novel collaborative framework named EffiQA that can strike a balance between performance and efficiency via an iterative paradigm. |
ZIXUAN DONG et. al. | arxiv-cs.CL | 2024-06-03 |
| 342 | Compositional 4D Dynamic Scenes Understanding with Physics Priors for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a video question answering dataset SuperCLEVR-Physics that focuses on the dynamics properties of objects. |
XINGRUI WANG et. al. | arxiv-cs.CV | 2024-06-02 |
| 343 | The Effect of Clustering Algorithms on Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View |
Rana Husni AlMahmoud; Marwah Alian; | Expert Syst. Appl. | 2024-06-01 |
| 344 | SPAGHETTI: Open-Domain Question Answering from Heterogeneous Data Sources with Retrieval and Semantic Parsing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce SPAGHETTI: Semantic Parsing Augmented Generation for Hybrid English information from Text Tables and Infoboxes, a hybrid question-answering (QA) pipeline that utilizes information from heterogeneous knowledge sources, including knowledge base, text, tables, and infoboxes. |
HEIDI C. ZHANG et. al. | arxiv-cs.CL | 2024-06-01 |
| 345 | Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking Via Side-by-Side Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a holistic pipeline for automatic data generation including question generation, answering, and model scoring using an “Evaluator”. |
BERND BOHNET et. al. | arxiv-cs.CL | 2024-05-31 |
| 346 | Passage-specific Prompt Tuning for Passage Reranking in Question Answering with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose passage-specific prompt tuning for reranking in open-domain question answering (PSPT): a parameter-efficient method that fine-tunes learnable passage-specific soft prompts, incorporating passage-specific knowledge from a limited set of question-passage relevance pairs. |
Xuyang Wu; Zhiyuan Peng; Krishna Sravanthi Rajanala Sai; Hsin-Tai Wu; Yi Fang; | arxiv-cs.CL | 2024-05-31 |
| 347 | VQA Training Sets Are Self-play Environments for Generating Few-shot Pools Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a technique in which existing training sets can be directly used for constructing computational environments with task metrics as rewards. |
Tautvydas Misiunas; Hassan Mansoor; Jasper Uijlings; Oriana Riva; Victor Carbune; | arxiv-cs.CV | 2024-05-30 |
| 348 | Video Question Answering for People with Visual Impairments Using An Egocentric 360-Degree Camera Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper addresses the daily challenges encountered by visually impaired individuals, such as limited access to information, navigation difficulties, and barriers to social interaction. To alleviate these challenges, we introduce a novel visual question answering dataset. |
Inpyo Song; Minjun Joo; Joonhyung Kwon; Jangwon Lee; | arxiv-cs.CV | 2024-05-30 |
| 349 | GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce GNN-RAG, a novel method for combining language understanding abilities of LLMs with the reasoning abilities of GNNs in a retrieval-augmented generation (RAG) style. |
Costas Mavromatis; George Karypis; | arxiv-cs.CL | 2024-05-30 |
| 350 | Encoding and Controlling Global Semantics for Long-form Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this fails to reason over the whole sequence of video, leading to sub-optimal performance. To address this problem, we introduce a state space layer (SSL) into multi-modal Transformer to efficiently integrate global semantics of the video, which mitigates the video information loss caused by frame and region selection modules. |
THONG THANH NGUYEN et. al. | arxiv-cs.CV | 2024-05-30 |
| 351 | Evaluating Zero-Shot GPT-4V Performance on 3D Visual Question Answering Benchmarks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As interest in reformulating the 3D Visual Question Answering (VQA) problem in the context of foundation models grows, it is imperative to assess how these new paradigms influence existing closed-vocabulary datasets. In this case study, we evaluate the zero-shot performance of foundational models (GPT-4 Vision and GPT-4) on well-established 3D VQA benchmarks, namely 3D-VQA and ScanQA. |
Simranjit Singh; Georgios Pavlakos; Dimitrios Stamoulis; | arxiv-cs.CV | 2024-05-29 |
| 352 | A Multi-Source Retrieval Question Answering Framework Based on RAG Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing RAG paradigms are inevitably influenced by erroneous retrieval information, thereby reducing the reliability and correctness of generated results. Therefore, to improve the relevance of retrieval information, this study proposes a method that replaces traditional retrievers with GPT-3.5, leveraging its vast corpus knowledge to generate retrieval information. |
RIDONG WU et. al. | arxiv-cs.IR | 2024-05-29 |
| 353 | MathChat: Benchmarking Mathematical Reasoning and Instruction Following in Multi-Turn Interactions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces MathChat, a comprehensive benchmark specifically designed to evaluate LLMs across a broader spectrum of mathematical tasks. |
ZHENWEN LIANG et. al. | arxiv-cs.AI | 2024-05-29 |
| 354 | Peering Into The Mind of Language Models: An Approach for Attribution in Contextual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a novel method for attribution in contextual question answering, leveraging the hidden state representations of LLMs. |
Anirudh Phukan; Shwetha Somasundaram; Apoorv Saxena; Koustava Goswami; Balaji Vasan Srinivasan; | arxiv-cs.CL | 2024-05-28 |
| 355 | Conv-CoA: Improving Open-domain Question Answering in Large Language Models Via Conversational Chain-of-Action Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a Conversational Chain-of-Action (Conv-CoA) framework for Open-domain Conversational Question Answering (OCQA). |
Zhenyu Pan; Haozheng Luo; Manling Li; Han Liu; | arxiv-cs.CL | 2024-05-28 |
| 356 | Hawk: Learning to Understand Open-World Video Anomalies Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce Hawk, a novel framework that leverages interactive large Visual Language Models (VLM) to interpret video anomalies precisely. |
JIAQI TANG et. al. | arxiv-cs.CV | 2024-05-27 |
| 357 | Cost-efficient Knowledge-based Question Answering with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose Coke, a novel cost-efficient strategy for KBQA with LLMs, modeled as a tailored multi-armed bandit problem to minimize calls to LLMs within limited budgets. |
JUNNAN DONG et. al. | arxiv-cs.CL | 2024-05-27 |
| 358 | Reason3D: Searching and Reasoning 3D Segmentation Via Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces Reason3D, a novel LLM designed for comprehensive 3D understanding. |
Kuan-Chih Huang; Xiangtai Li; Lu Qi; Shuicheng Yan; Ming-Hsuan Yang; | arxiv-cs.CV | 2024-05-27 |
| 359 | Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For example, if the LLM is equally likely to output two contradicting answers to the same question, then its generated response should reflect this uncertainty by hedging its answer (e.g., I’m not sure, but I think…). We formalize faithful response uncertainty based on the gap between the model’s intrinsic confidence in the assertions it makes and the decisiveness by which they are conveyed. |
Gal Yona; Roee Aharoni; Mor Geva; | arxiv-cs.CL | 2024-05-27 |
| 360 | THREAD: Thinking Deeper with Recursive Spawning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Large language models (LLMs) have shown impressive capabilities across diverse settings, but still struggle as the length and complexity of the context increases. To address this challenge, we propose Thinking Recursively and Dynamically (ThReaD). |
Philip Schroeder; Nathaniel Morgan; Hongyin Luo; James Glass; | arxiv-cs.CL | 2024-05-27 |
| 361 | Accurate and Nuanced Open-QA Evaluation Through Textual Entailment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose to study the entailment relations of answers to identify more informative and more general system answers, offering a much closer evaluation to human judgment on both NaturalQuestions and TriviaQA while being learning-free. |
Peiran Yao; Denilson Barbosa; | arxiv-cs.CL | 2024-05-26 |
| 362 | Map-based Modular Approach for Zero-shot Embodied Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a map-based modular EQA method that enables real robots to navigate unknown environments through frontier-based map creation and address unknown QA pairs using foundation models that support open vocabulary. |
Koya Sakamoto; Daichi Azuma; Taiki Miyanishi; Shuhei Kurita; Motoaki Kawanabe; | arxiv-cs.RO | 2024-05-26 |
| 363 | Crafting Interpretable Embeddings By Asking LLMs Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce question-answering embeddings (QA-Emb), embeddings where each feature represents an answer to a yes/no question asked to an LLM. |
VINAMRA BENARA et. al. | arxiv-cs.CL | 2024-05-26 |
| 364 | Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: For each task, we review their relevant characteristics, sub-tasks, and specific challenges (e.g., missing datasets for multi-document summarization, coherence in story generation, and complex reasoning for question answering). |
Jonas Becker; Jan Philip Wahle; Bela Gipp; Terry Ruas; | arxiv-cs.CL | 2024-05-24 |
| 365 | Efficient Medical Question Answering with Knowledge-Augmented Question Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a method to improve the proficiency of a small language model in the medical domain by employing a two-fold approach. |
JULIEN KHLAUT et. al. | arxiv-cs.CL | 2024-05-23 |
| 366 | LOVA3: Learning to Visual Question Answering, Asking and Assessment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, current Multimodal Large Language Models (MLLMs) primarily focus on question answering, often neglecting the full potential of questioning and assessment skills. In this study, we introduce LOVA3, an innovative framework named “Learning tO Visual Question Answering, Asking and Assessment,” designed to equip MLLMs with these additional capabilities. |
Henry Hengyuan Zhao; Pan Zhou; Difei Gao; Mike Zheng Shou; | arxiv-cs.CV | 2024-05-23 |
| 367 | PitVQA: Image-grounded Text Embedding LLM for Visual Question Answering in Pituitary Surgery Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces PitVQA, a novel dataset specifically designed for VQA in endonasal pituitary surgery and PitVQA-Net, an adaptation of the GPT2 with a novel image-grounded text embedding for surgical VQA. |
RUNLONG HE et. al. | arxiv-cs.CV | 2024-05-22 |
| 368 | Large Language Models Can Self-Correct with Minimal Effort Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an iterative verify-then-correct framework to progressively identify and correct (probably) false responses, named ProCo. |
ZHENYU WU et. al. | arxiv-cs.CL | 2024-05-22 |
| 369 | Efficient and Interpretable Information Retrieval for Product Question Answering with Heterogeneous Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we explore the potential of jointly learning dense semantic representation and combining it with the lexical one for ranking candidate information. |
Biplob Biswas; Rajiv Ramnath; | arxiv-cs.LG | 2024-05-21 |
| 370 | Dataset and Benchmark for Urdu Natural Scenes Text Detection, Recognition and Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a new multi-task Urdu scene text dataset comprising over 1000 natural scene images, which can be used for text detection, recognition, and VQA tasks. |
HIBA MARYAM et. al. | arxiv-cs.CV | 2024-05-21 |
| 371 | OLAPH: Improving Factuality in Biomedical Long-form Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Thus, we introduce MedLFQA, a benchmark dataset reconstructed using long-form question-answering datasets related to the biomedical domain. |
Minbyul Jeong; Hyeon Hwang; Chanwoong Yoon; Taewhoo Lee; Jaewoo Kang; | arxiv-cs.CL | 2024-05-21 |
| 372 | MentalQA: An Annotated Arabic Corpus for Questions and Answers of Mental Healthcare Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce MentalQA, a novel Arabic dataset featuring conversational-style question-and-answer (QA) interactions. |
Hassan Alhuzali; Ashwag Alasmari; Hamad Alsaleh; | arxiv-cs.CL | 2024-05-21 |
| 373 | MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we tackle multilingual TEC-VQA by introducing MTVQA, the first benchmark featuring high-quality human expert annotations across 9 diverse languages, consisting of 6,778 question-answer pairs across 2,116 images. |
JINGQUN TANG et. al. | arxiv-cs.CV | 2024-05-20 |
| 374 | Increasing The LLM Accuracy for Question Answering: Ontologies to The Rescue! Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Building on the observations of our previous research where the inaccurate LLM-generated SPARQL queries followed incorrect paths, we present an approach that consists of 1) Ontology-based Query Check (OBQC): detects errors by leveraging the ontology of the knowledge graph to check if the LLM-generated SPARQL query matches the semantic of ontology and 2) LLM Repair: use the error explanations with an LLM to repair the SPARQL query. |
Dean Allemang; Juan Sequeda; | arxiv-cs.AI | 2024-05-19 |
| 375 | MemeMQA: Multimodal Question Answering for Memes Via Rationale-Based Inferencing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To extend this research, we introduce MemeMQA, a multimodal question-answering framework aiming to solicit accurate responses to structured questions while providing coherent explanations. |
Siddhant Agarwal; Shivam Sharma; Preslav Nakov; Tanmoy Chakraborty; | arxiv-cs.CL | 2024-05-18 |
| 376 | StackOverflowVQA: Stack Overflow Visual Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we focus on the questions which need the understanding of images in addition to the question itself. |
Motahhare Mirzaei; Mohammad Javad Pirhadi; Sauleh Eetemadi; | arxiv-cs.CV | 2024-05-17 |
| 377 | 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 |
| 378 | SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce SciQAG, a novel framework for automatically generating high-quality science question-answer pairs from a large corpus of scientific literature based on large language models (LLMs). |
YUWEI WAN et. al. | arxiv-cs.CL | 2024-05-16 |
| 379 | Towards Better Question Generation in QA-based Event Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, in QA-based EE, the quality of the questions dramatically affects the extraction accuracy, and how to generate high-quality questions for QA-based EE remains a challenge. In this work, to tackle this challenge, we suggest four criteria to evaluate the quality of a question and propose a reinforcement learning method, RLQG, for QA-based EE that can generate generalizable, high-quality, and context-dependent questions and provides clear guidance to QA models. |
Zijin Hong; Jian Liu; | arxiv-cs.CL | 2024-05-16 |
| 380 | 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; | arxiv-cs.HC | 2024-05-16 |
| 381 | KnowledgeHub: An End-to-end Tool for Assisted Scientific Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper describes the KnowledgeHub tool, a scientific literature Information Extraction (IE) and Question Answering (QA) pipeline. |
SHINNOSUKE TANAKA et. al. | arxiv-cs.IR | 2024-05-16 |
| 382 | STAR: A Benchmark for Situated Reasoning in Real-World Videos IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts 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 |
| 383 | 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 |
| 384 | 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 |
| 385 | 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 |
| 386 | 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 |
| 387 | FreeVA: Offline MLLM As Training-Free Video Assistant Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 388 | Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 389 | 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 |
| 390 | 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 |
| 391 | 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 |
| 392 | MedConceptsQA: Open Source Medical Concepts QA Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 393 | 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 |
| 394 | 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 |
| 395 | 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 |
| 396 | 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 |
| 397 | 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 |
| 398 | 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 |
| 399 | 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 |
| 400 | Overview of The EHRSQL 2024 Shared Task on Reliable Text-to-SQL Modeling on Electronic Health Records Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we describe the task of reliable text-to-SQL modeling, the dataset, and the methods and results of the participants. |
Gyubok Lee; Sunjun Kweon; Seongsu Bae; Edward Choi; | arxiv-cs.CL | 2024-05-04 |
| 401 | 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 |
| 402 | 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 Related Code 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 |
| 403 | UQA: Corpus for Urdu Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 404 | 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 |
| 405 | Question-Aware Global-Local Video Understanding Network for Audio-Visual Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: As a newly emerging task, audio-visual question answering (AVQA) has attracted research attention. Compared with traditional single-modality (e.g., audio or visual) QA tasks, it … |
Zailong Chen; Lei Wang; Peng Wang; Peng Gao; | IEEE Transactions on Circuits and Systems for Video … | 2024-05-01 |
| 406 | ZVQAF: Zero-shot Visual Question Answering with Feedback from Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View |
Cheng Liu; Chao Wang; Yan Peng; Zhixu Li; | Neurocomputing | 2024-05-01 |
| 407 | Video Question Answering With Semantic Disentanglement and Reasoning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Video question answering aims to provide correct answers given complex videos and related questions, posting high requirements of the comprehension ability in both video and … |
Jin Liu; Guoxiang Wang; Jialong Xie; F. Zhou; Huijuan Xu; | IEEE Transactions on Circuits and Systems for Video … | 2024-05-01 |
| 408 | 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 |
| 409 | When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 410 | 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 |
| 411 | TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 412 | Multi-Page Document Visual Question Answering Using Self-Attention Scoring Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 413 | 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 |
| 414 | 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 |
| 415 | 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 |
| 416 | 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 |
| 417 | Large Language Models in The Clinic: A Comprehensive Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To better understand LLMs in the clinic, we construct a benchmark ClinicBench. |
ANDREW LIU et. al. | arxiv-cs.CL | 2024-04-25 |
| 418 | 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 |
| 419 | 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 |
| 420 | Assessing The Potential Of Mid-Sized Language Models For Clinical QA Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Large language models, such as GPT-4 and Med-PaLM, have shown impressive performance on clinical tasks; however, they require access to compute, are closed-source, and cannot be … |
ELLIOT BOLTON et. al. | ArXiv | 2024-04-24 |
| 421 | Evaluating Tool-Augmented Agents in Remote Sensing Platforms Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Tool-augmented Large Language Models (LLMs) have shown impressive capabilities in remote sensing (RS) applications. However, existing benchmarks assume question-answering input … |
Simranjit Singh; Michael Fore; Dimitrios Stamoulis; | ArXiv | 2024-04-23 |
| 422 | 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 |
| 423 | 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 Related Code 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 |
| 424 | RS-LLaVA: A Large Vision-Language Model for Joint Captioning and Question Answering in Remote Sensing Imagery Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In this paper, we delve into the innovative application of large language models (LLMs) and their extension, large vision-language models (LVLMs), in the field of remote sensing … |
Y. Bazi; Laila Bashmal; Mohamad Mahmoud Al Rahhal; Riccardo Ricci; F. Melgani; | Remote. Sens. | 2024-04-23 |
| 425 | 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 |
| 426 | 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 |
| 427 | Self-Bootstrapped Visual-Language Model for Knowledge Selection and Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, the retrieved knowledge is not truly conducive to helping answer the question, affecting the performance of the overall system. To address this issue, we propose a novel framework that leverages the visual-language model to select the key knowledge retrieved by DPR and answer questions. |
Dongze Hao; Qunbo Wang; Longteng Guo; Jie Jiang; Jing Liu; | arxiv-cs.CV | 2024-04-22 |
| 428 | Listen Then See: Video Alignment with Speaker Attention Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 429 | Exploring Diverse Methods in Visual Question Answering IF:3 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 |
| 430 | MahaSQuAD: Bridging Linguistic Divides in Marathi Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 431 | 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 |
| 432 | LaPA: Latent Prompt Assist Model For Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 433 | 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 |
| 434 | Look, Listen, and Answer: Overcoming Biases for Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 435 | Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models IF:3 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 |
| 436 | 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; Jaione Bengoetxea; | arxiv-cs.CL | 2024-04-18 |
| 437 | Characterizing LLM Abstention Behavior in Science QA with Context Perturbations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 438 | 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 |
| 439 | 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 |
| 440 | 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 |
| 441 | 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 |
| 442 | 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 |
| 443 | Spiral of Silence: 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 |
| 444 | 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 |
| 445 | 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 |
| 446 | 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 |
| 447 | 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 |
| 448 | 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 |
| 449 | 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 |
| 450 | 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 |
| 451 | 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 |
| 452 | MM-PhyQA: Multimodal Physics Question-Answering with Multi-image CoT Prompting Related Papers Related Patents Related Grants Related Venues Related Experts View |
AVINASH ANAND et. al. | Pacific-Asia Conference on Knowledge Discovery and Data … | 2024-04-11 |
| 453 | 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 |
| 454 | 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 |
| 455 | 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 |
| 456 | Early Prediction of Promising Expert Users on Community Question Answering Sites Related Papers Related Patents Related Grants Related Venues Related Experts View |
P. Roy; Jyoti Prakash Singh; | Int. J. Syst. Assur. Eng. Manag. | 2024-04-09 |
| 457 | 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 |
| 458 | 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 |
| 459 | 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 |
| 460 | 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 |
| 461 | 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 |
| 462 | 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 |
| 463 | 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 |
| 464 | 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 |
| 465 | 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 |
| 466 | 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 |
| 467 | CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Model (LLM) output by providing prior knowledge as context to input. This is beneficial for knowledge-intensive and … |
N. WIRATUNGA et. al. | International Conference on Case-Based Reasoning | 2024-04-04 |
| 468 | 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 |
| 469 | Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 470 | 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 |
| 471 | 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 |
| 472 | 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 |
| 473 | 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 |
| 474 | 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 |
| 475 | 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 |
| 476 | 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 |
| 477 | 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 Related Code View Highlight: We identify more advanced/explicit causal relationship modeling & joint modeling of vision and language as the immediate areas for future efforts to focus upon. |
PARITOSH PARMAR et. al. | arxiv-cs.CV | 2024-04-01 |
| 478 | 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 |
| 479 | 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 |
| 480 | 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 |
| 481 | 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 |
| 482 | 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 |
| 483 | 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 |
| 484 | 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 |
| 485 | JDocQA: Japanese Document Question Answering Dataset for Generative Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 486 | Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To tackle the problem, we propose the Retrieval-Augmented model Editing (RAE) framework for multi-hop question answering. |
YUCHENG SHI et. al. | arxiv-cs.CL | 2024-03-28 |
| 487 | 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 |
| 488 | An Image Grid Can Be Worth A Video: Zero-shot Video Question Answering Using A VLM IF:3 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 |
| 489 | 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 |
| 490 | 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 |
| 491 | 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 |
| 492 | 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 |
| 493 | 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 |
| 494 | 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 |
| 495 | 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 |
| 496 | 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 |
| 497 | 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 |
| 498 | 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 |
| 499 | Task-Agnostic Detector for Insertion-Based Backdoor Attacks IF:3 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 |
| 500 | 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 |
| 501 | RetLLM-E: Retrieval-Prompt Strategy for Question-Answering on Student Discussion Forums Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This paper focuses on using Large Language Models to support teaching assistants in answering questions on large student forums such as Piazza and EdSTEM. Since student questions … |
CHANCHARIK MITRA et. al. | AAAI Conference on Artificial Intelligence | 2024-03-24 |
| 502 | Graph Reasoning Transformers for Knowledge-Aware Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Augmenting Language Models (LMs) with structured knowledge graphs (KGs) aims to leverage structured world knowledge to enhance the capability of LMs to complete … |
Ruilin Zhao; Feng Zhao; Liang Hu; Guandong Xu; | AAAI Conference on Artificial Intelligence | 2024-03-24 |
| 503 | SciSpace Copilot: Empowering Researchers Through Intelligent Reading Assistance Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We introduce SciSpace Copilot, an AI research assistant that helps in understanding and reading research papers faster by providing a plethora of features. Answering questions … |
TRINITA ROY et. al. | AAAI Conference on Artificial Intelligence | 2024-03-24 |
| 504 | CyberQ: Generating Questions and Answers for Cybersecurity Education Using Knowledge Graph-Augmented LLMs Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Building a skilled cybersecurity workforce is paramount to building a safer digital world. However, the diverse skill set, constantly emerging vulnerabilities, and deployment of … |
Garima Agrawal; Kuntal Pal; Yuli Deng; Huanmin Liu; Yingying Chen; | AAAI Conference on Artificial Intelligence | 2024-03-24 |
| 505 | 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 |
| 506 | 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 awakened. 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, thereby awakening relevant knowledge in LLMs without relying on external resources. |
HUANXUAN LIAO et. al. | arxiv-cs.CL | 2024-03-22 |
| 507 | Surgical-LVLM: Learning to Adapt Large Vision-Language Model for Grounded Visual Question Answering in Robotic Surgery Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Recent advancements in Surgical Visual Question Answering (Surgical-VQA) and related region grounding have shown great promise for robotic and medical applications, addressing the … |
GUAN-FENG WANG et. al. | ArXiv | 2024-03-22 |
| 508 | Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models Through Question Complexity IF:3 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 |
| 509 | 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 |
| 510 | 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 |
| 511 | 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 |
| 512 | 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 |
| 513 | 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 |
| 514 | 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 |
| 515 | 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 Related Code 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 |
| 516 | 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 |
| 517 | RetinaQA: A Robust Knowledge Base Question Answering Model for Both Answerable and Unanswerable Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recent research has found that such models, when superficially adapted to detect answerability, struggle to satisfactorily identify the different categories of unanswerable questions, and simultaneously preserve good performance for answerable questions. Towards addressing this issue, we propose RetinaQA, a new KBQA model that unifies two key ideas in a single KBQA architecture: (a) discrimination over candidate logical forms, rather than generating these, for handling schema-related unanswerability, and (b) sketch-filling-based construction of candidate logical forms for handling data-related unaswerability. |
Prayushi Faldu; Indrajit Bhattacharya; | arxiv-cs.CL | 2024-03-16 |
| 518 | 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 |
| 519 | 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 |
| 520 | 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 |
| 521 | 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 |
| 522 | RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To systematically find the optimal configuration, we introduce RAGGED, a framework for analyzing RAG configurations across various DBQA tasks. |
Jennifer Hsia; Afreen Shaikh; Zhiruo Wang; Graham Neubig; | arxiv-cs.CL | 2024-03-13 |
| 523 | 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 |
| 524 | Answering Diverse Questions Via Text Attached with Key Audio-Visual Clues Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 525 | 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 |
| 526 | InfiBench: Evaluating The Question-Answering Capabilities of Code Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, they are insufficient to cover the full range of expected capabilities of code LLMs, which span beyond code generation to answering diverse coding-related questions. To fill this gap, we propose InfiBench, the first large-scale freeform question-answering (QA) benchmark for code to our knowledge, comprising 234 carefully selected high-quality Stack Overflow questions that span across 15 programming languages. |
LINYI LI et. al. | arxiv-cs.SE | 2024-03-10 |
| 527 | 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 Related Code View Highlight: In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) along with ranking and re-ranking techniques, to improve the factuality of long-form question answering (QA) in the medical domain. |
RUI YANG et. al. | arxiv-cs.CL | 2024-03-09 |
| 528 | 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 |
| 529 | 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 |
| 530 | 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 |
| 531 | 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 |
| 532 | 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 |
| 533 | Evaluating The Elementary Multilingual Capabilities of Large Language Models with MultiQ IF:3 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 |
| 534 | Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 535 | 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 |
| 536 | 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 |
| 537 | 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 |
| 538 | Automatic Question-Answer Generation for Long-Tail Knowledge Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Pretrained Large Language Models (LLMs) have gained significant attention for addressing open-domain Question Answering (QA). While they exhibit high accuracy in answering … |
ROHAN KUMAR et. al. | ArXiv | 2024-03-03 |
| 539 | 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 |
| 540 | XMQAs: Constructing Complex-Modified Question-Answering Dataset for Robust Question Understanding Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Question understanding is an important issue to the success of a Knowledge-based Question Answering (KBQA) system.However, the existing study does not pay enough attention to this … |
Yuyan Chen; Yanghua Xiao; Zhixu Li; Bang Liu; | IEEE Transactions on Knowledge and Data Engineering | 2024-03-01 |
| 541 | 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 |
| 542 | 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 |
| 543 | 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 |
| 544 | A Cognitive Evaluation Benchmark of Image Reasoning and Description for Large Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 545 | 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 |
| 546 | 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 |
| 547 | 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 |
| 548 | 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 Related Code 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) during the fine-tuning phase. |
Junda Wang; Zhichao Yang; Zonghai Yao; Hong Yu; | arxiv-cs.CL | 2024-02-27 |
| 549 | 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 |
| 550 | 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 |
| 551 | 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 |
| 552 | 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 |
| 553 | GigaPevt: Multimodal Medical Assistant Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This demo paper presents the GigaPevt, the first multimodal medical assistant that combines the dialog capabilities of large language models with specialized medical models. |
PAVEL BLINOV et. al. | arxiv-cs.AI | 2024-02-26 |
| 554 | Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Temporal knowledge graph question answering (TKGQA) poses a significant challenge task, due to the temporal constraints hidden in questions and the answers sought from dynamic … |
YIFU GAO et. al. | arxiv-cs.CL | 2024-02-26 |
| 555 | SuRe: Summarizing Retrievals Using Answer Candidates for Open-domain QA of LLMs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 556 | Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 557 | 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 |
| 558 | 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 |
| 559 | 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 |
| 560 | 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 |
| 561 | 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 |
| 562 | 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 |
| 563 | 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 |
| 564 | Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Research IF:3 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 |
| 565 | 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 |
| 566 | 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 |
| 567 | Biomedical Entity Linking As Multiple Choice Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code 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 |
| 568 | 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 |
| 569 | CommVQA: Situating Visual Question Answering in Communicative Contexts Related Papers Related Patents Related Grants |