Paper Digest: EMNLP 2023 Highlights
1, Is ChatGPT A General-Purpose Natural Language Processing Task Solver?
Chengwei Qin; Aston Zhang; Zhuosheng Zhang; Jiaao Chen; Michihiro Yasunaga; Diyi Yang;
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Highlight: In this work, we empirically analyze the zero-shot learning ability of ChatGPT by evaluating it on 20 popular NLP datasets covering 7 representative task categories.
2, Lifelong Sequence Generation with Dynamic Module Expansion and Adaptation
Chengwei Qin; Chen Chen; Shafiq Joty;
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Highlight: Inspired by the learning paradigm of humans, we propose Dynamic Module Expansion and Adaptation (DMEA), which enables the model to dynamically determine the architecture for acquiring new knowledge based on task correlation and select the most similar previous tasks to facilitate adaptation to new tasks.
3, FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
Sewon Min; Kalpesh Krishna; Xinxi Lyu; Mike Lewis; Wen-tau Yih; Pang Koh; Mohit Iyyer; Luke Zettlemoyer; Hannaneh Hajishirzi;
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Highlight: In this paper, we introduce FACTSCORE, a new evaluation that breaks a generation into a series of atomic facts and computes the percentage of atomic facts supported by a reliable knowledge source.
4, Automatic Prompt Optimization with ?Gradient Descent? and Beam Search
Reid Pryzant; Dan Iter; Jerry Li; Yin Lee; Chenguang Zhu; Michael Zeng;
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Highlight: Large Language Models (LLMs) have shown impressive performance as general purpose agents, but their abilities remain highly dependent on prompts which are hand written with onerous trial-and-error effort. We propose a simple and nonparametric solution to this problem, Prompt Optimization with Textual Gradients (ProTeGi), which is inspired by numerical gradient descent to automatically improve prompts, assuming access to training data and an LLM API.
5, Language Models with Rationality
Nora Kassner; Oyvind Tafjord; Ashish Sabharwal; Kyle Richardson; Hinrich Schuetze; Peter Clark;
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Highlight: This lack of interpretability is a growing impediment to widespread use of LLMs. To address this, our goals are to make model beliefs and their inferential relationships explicit, and to resolve inconsistencies that may exist, so that answers are supported by interpretable chains of reasoning drawn from a consistent network of beliefs.
6, Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data
Canwen Xu; Daya Guo; Nan Duan; Julian McAuley;
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Highlight: We propose a pipeline that can automatically generate a high-quality multi-turn chat corpus by leveraging ChatGPT to engage in a conversation with itself.
7, Reasoning with Language Model Is Planning with World Model
Shibo Hao; Yi Gu; Haodi Ma; Joshua Hong; Zhen Wang; Daisy Wang; Zhiting Hu;
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Highlight: To overcome the limitations, we propose a new LLM reasoning framework, Reasoning via Planning (RAP).
8, Revisiting Machine Translation for Cross-lingual Classification
Mikel Artetxe; Vedanuj Goswami; Shruti Bhosale; Angela Fan; Luke Zettlemoyer;
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Highlight: We show that, by using a stronger MT system and mitigating the mismatch between training on original text and running inference on machine translated text, translate-test can do substantially better than previously assumed.
9, API-Assisted Code Generation for Question Answering on Varied Table Structures
Yihan Cao; Shuyi Chen; Ryan Liu; Zhiruo Wang; Daniel Fried;
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Highlight: In response, this paper introduces a unified TableQA framework that: (1) provides a unified representation for structured tables as multi-index Pandas data frames, (2) uses Python as a powerful querying language, and (3) uses few-shot prompting to translate NL questions into Python programs, which are executable on Pandas data frames.
10, Navigating The Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models
Kaitlyn Zhou; Dan Jurafsky; Tatsunori Hashimoto;
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Highlight: The increased deployment of LMs for real-world tasks involving knowledge and facts makes it important to understand model epistemology: what LMs think they know, and how their attitudes toward that knowledge are affected by language use in their inputs. Here, we study an aspect of model epistemology: how epistemic markers of certainty, uncertainty, or evidentiality like ?I?m sure it?s?, ?I think it?s?, or ?Wikipedia says it?s? affect models, and whether they contribute to model failures.
11, SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
Potsawee Manakul; Adian Liusie; Mark Gales;
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Highlight: In this work, we propose ?SelfCheckGPT?, a simple sampling-based approach that can be used to fact-check the responses of black-box models in a zero-resource fashion, i. e. without an external database.
12, C-STS: Conditional Semantic Textual Similarity
Ameet Deshpande; Carlos Jimenez; Howard Chen; Vishvak Murahari; Victoria Graf; Tanmay Rajpurohit; Ashwin Kalyan; Danqi Chen; Karthik Narasimhan;
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Highlight: However, it is an inherently ambiguous task, with the sentence similarity depending on the specific aspect of interest. We resolve this ambiguity by proposing a novel task called conditional STS (C-STS) which measures similarity conditioned on an aspect elucidated in natural language (hereon, condition).
13, Transcending Scaling Laws with 0.1% Extra Compute
Yi Tay; Jason Wei; Hyung Chung; Vinh Tran; David So; Siamak Shakeri; Xavier Garcia; Steven Zheng; Jinfeng Rao; Aakanksha Chowdhery; Denny Zhou; Donald Metzler; Slav Petrov; Neil Houlsby; Quoc Le; Mostafa Dehghani;
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Highlight: In this paper, we continue training a baseline language model, PaLM, with ULR2, introducing a new set of models at 8B, 62B, and 540B scale which we call U-PaLM.
14, RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation
Fengji Zhang; Bei Chen; Yue Zhang; Jacky Keung; Jin Liu; Daoguang Zan; Yi Mao; Jian-Guang Lou; Weizhu Chen;
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Highlight: We propose RepoCoder, a simple, generic, and effective framework to address the challenge.
15, Active Retrieval Augmented Generation
Zhengbao Jiang; Frank Xu; Luyu Gao; Zhiqing Sun; Qian Liu; Jane Dwivedi-Yu; Yiming Yang; Jamie Callan; Graham Neubig;
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Highlight: In this work, we provide a generalized view of active retrieval augmented generation, methods that actively decide when and what to retrieve across the course of the generation.
16, MEGA: Multilingual Evaluation of Generative AI
Kabir Ahuja; Harshita Diddee; Rishav Hada; Millicent Ochieng; Krithika Ramesh; Prachi Jain; Akshay Nambi; Tanuja Ganu; Sameer Segal; Mohamed Ahmed; Kalika Bali; Sunayana Sitaram;
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Highlight: We present a thorough analysis of the performance of models across languages and tasks and discuss challenges in improving the performance of generative LLMs on low-resource languages.
17, CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion
Xingwei He; Yeyun Gong; A-Long Jin; Hang Zhang; Anlei Dong; Jian Jiao; Siu Yiu; Nan Duan;
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Highlight: In this paper, we propose a curriculum sampling strategy that utilizes pseudo queries during training and progressively enhances the relevance between the generated query and the real query.
18, Document-Level Machine Translation with Large Language Models
Longyue Wang; Chenyang Lyu; Tianbo Ji; Zhirui Zhang; Dian Yu; Shuming Shi; Zhaopeng Tu;
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Highlight: The study focuses on three aspects: 1) Effects of Context-Aware Prompts, where we investigate the impact of different prompts on document-level translation quality and discourse phenomena; 2) Comparison of Translation Models, where we compare the translation performance of ChatGPT with commercial MT systems and advanced document-level MT methods; 3) Analysis of Discourse Modelling Abilities, where we further probe discourse knowledge encoded in LLMs and shed light on impacts of training techniques on discourse modeling.
19, We?re Afraid Language Models Aren?t Modeling Ambiguity
Alisa Liu; Zhaofeng Wu; Julian Michael; Alane Suhr; Peter West; Alexander Koller; Swabha Swayamdipta; Noah Smith; Yejin Choi;
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Highlight: We capture ambiguity in a sentence through its effect on entailment relations with another sentence, and collect AmbiEnt, a linguist-annotated benchmark of 1,645 examples with diverse kinds of ambiguity.
20, CoMPosT: Characterizing and Evaluating Caricature in LLM Simulations
Myra Cheng; Tiziano Piccardi; Diyi Yang;
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Highlight: Moreover, there is growing concern that these LLM simulations are flattened caricatures of the personas that they aim to simulate, failing to capture the multidimensionality of people and perpetuating stereotypes. To bridge these gaps, we present CoMPosT, a framework to characterize LLM simulations using four dimensions: Context, Model, Persona, and Topic.
21, Answering Questions By Meta-Reasoning Over Multiple Chains of Thought
Ori Yoran; Tomer Wolfson; Ben Bogin; Uri Katz; Daniel Deutch; Jonathan Berant;
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Highlight: We introduce Multi-Chain Reasoning (MCR), an approach which prompts large language models to meta-reason over multiple chains of thought, rather than aggregate their answers.
22, Reward-Augmented Decoding: Efficient Controlled Text Generation With A Unidirectional Reward Model
Haikang Deng; Colin Raffel;
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Highlight: In this paper, we introduce Reward-Augmented Decoding (RAD), a text generation procedure that uses a small unidirectional reward model to encourage a language model to generate text that has certain properties.
23, ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering Over Knowledge Graph
Jinhao Jiang; Kun Zhou; Xin Zhao; Yaliang Li; Ji-Rong Wen;
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Highlight: Despite the effectiveness, due to the divergence in model architecture, the PLM and GNN are not closely integrated, limiting the knowledge sharing and fine-grained feature interactions. To solve it, we aim to simplify the above two-module approach, and develop a more capable PLM that can directly support subgraph reasoning for KGQA, namely ReasoningLM.
24, StructGPT: A General Framework for Large Language Model to Reason Over Structured Data
Jinhao Jiang; Kun Zhou; Zican Dong; Keming Ye; Xin Zhao; Ji-Rong Wen;
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Highlight: In this paper, we aim to improve the reasoning ability of large language models (LLMs) over structured data in a unified way.
25, Contrastive Learning for Inference in Dialogue
Etsuko Ishii; Yan Xu; Bryan Wilie; Ziwei Ji; Holy Lovenia; Willy Chung; Pascale Fung;
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Highlight: In this paper, we analyze the behavior of the models based on the task difficulty defined by the semantic information gap ? which distinguishes inductive and deductive reasoning.
26, LM Vs LM: Detecting Factual Errors Via Cross Examination
Roi Cohen; May Hamri; Mor Geva; Amir Globerson;
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Highlight: Inspired by truth-seeking mechanisms in law, we propose a factuality evaluation framework for LMs that is based on cross-examination.
27, Query2doc: Query Expansion with Large Language Models
Liang Wang; Nan Yang; Furu Wei;
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Highlight: This paper introduces a simple yet effective query expansion approach, denoted as query2doc, to improve both sparse and dense retrieval systems.
28, XLM-V: Overcoming The Vocabulary Bottleneck in Multilingual Masked Language Models
Davis Liang; Hila Gonen; Yuning Mao; Rui Hou; Naman Goyal; Marjan Ghazvininejad; Luke Zettlemoyer; Madian Khabsa;
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Highlight: In this paper, we introduce a new approach for scaling to very large multilingual vocabularies by de-emphasizing token sharing between languages with little lexical overlap and assigning vocabulary capacity to achieve sufficient coverage for each individual language.
29, WiCE: Real-World Entailment for Claims in Wikipedia
Ryo Kamoi; Tanya Goyal; Juan Rodriguez; Greg Durrett;
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Highlight: We propose WiCE, a new fine-grained textual entailment dataset built on natural claim and evidence pairs extracted from Wikipedia.
30, TaskWeb: Selecting Better Source Tasks for Multi-task NLP
Joongwon Kim; Akari Asai; Gabriel Ilharco; Hannaneh Hajishirzi;
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Highlight: In this work, we investigate whether knowing task relationships via pairwise task transfer improves choosing one or more source tasks that help to learn a new target task.
31, Query Rewriting in Retrieval-Augmented Large Language Models
Xinbei Ma; Yeyun Gong; Pengcheng He; Hai Zhao; Nan Duan;
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Highlight: This work introduces a new framework, Rewrite-Retrieve-Read instead of the previous retrieve-then-read for the retrieval-augmented LLMs from the perspective of the query rewriting.
32, G-Eval: NLG Evaluation Using Gpt-4 with Better Human Alignment
Yang Liu; Dan Iter; Yichong Xu; Shuohang Wang; Ruochen Xu; Chenguang Zhu;
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Highlight: In this work, we present G-Eval, a framework of using large language models with chain-of-thoughts (CoT) and a form-filling paradigm, to assess the quality of NLG outputs.
33, TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models
Zorik Gekhman; Jonathan Herzig; Roee Aharoni; Chen Elkind; Idan Szpektor;
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Highlight: Alternatively, large language models (LLMs) have recently shown promising results in directly evaluating generative tasks, but are too computationally expensive for practical use. Motivated by these limitations, we introduce TrueTeacher, a method for generating synthetic data by annotating diverse model-generated summaries using a LLM.
34, Poisoning Retrieval Corpora By Injecting Adversarial Passages
Zexuan Zhong; Ziqing Huang; Alexander Wettig; Danqi Chen;
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Highlight: In this work, we propose a novel attack for dense retrieval systems in which a malicious user generates a small number of adversarial passages by perturbing discrete tokens to maximize similarity with a provided set of training queries.
35, MQuAKE: Assessing Knowledge Editing in Language Models Via Multi-Hop Questions
Zexuan Zhong; Zhengxuan Wu; Christopher Manning; Christopher Potts; Danqi Chen;
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Highlight: In this work, we present a benchmark MQuAKE (Multi-hop Question Answering for Knowledge Editing) comprising multi-hop questions that assess whether edited models correctly answer questions where the answer should change as an entailed consequence of edited facts.
36, FactKB: Generalizable Factuality Evaluation Using Language Models Enhanced with Factual Knowledge
Shangbin Feng; Vidhisha Balachandran; Yuyang Bai; Yulia Tsvetkov;
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Highlight: We propose FactKB?a simple new approach to factuality evaluation that is generalizable across domains, in particular with respect to entities and relations.
37, Batch Prompting: Efficient Inference with Large Language Model APIs
Zhoujun Cheng; Jungo Kasai; Tao Yu;
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Highlight: We propose batch prompting, a simple yet effective prompting approach that enables the LLM to run inference in batches, instead of one sample at a time.
38, Dissecting Recall of Factual Associations in Auto-Regressive Language Models
Mor Geva; Jasmijn Bastings; Katja Filippova; Amir Globerson;
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Highlight: While previous work looked into where factual associations are stored, only little is known about how they are retrieved internally during inference. We investigate this question through the lens of information flow.
39, The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations
Vipula Rawte; Swagata Chakraborty; Agnibh Pathak; Anubhav Sarkar; S.M Towhidul Islam Tonmoy; Aman Chadha; Amit Sheth; Amitava Das;
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Highlight: In conclusion, we propose two solution strategies for mitigating hallucinations.
40, MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding
Steven Wang; Antoine Scardigli; Leonard Tang; Wei Chen; Dmitry Levkin; Anya Chen; Spencer Ball; Thomas Woodside; Oliver Zhang; Dan Hendrycks;
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Highlight: Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association?s 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations.
41, AnyTOD: A Programmable Task-Oriented Dialog System
Jeffrey Zhao; Yuan Cao; Raghav Gupta; Harrison Lee; Abhinav Rastogi; Mingqiu Wang; Hagen Soltau; Izhak Shafran; Yonghui Wu;
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Highlight: We propose AnyTOD, an end-to-end, zero-shot task-oriented dialog (TOD) system capable of zero-shot adaptation onto unseen tasks or domains.
42, PALS: Personalized Active Learning for Subjective Tasks in NLP
Kamil Kanclerz; Konrad Karanowski; Julita Bielaniewicz; Marcin Gruza; Piotr Milkowski; Jan Kocon; Przemyslaw Kazienko;
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Highlight: In this paper, we present novel Personalized Active Learning techniques for Subjective NLP tasks (PALS) to either reduce the cost of the annotation process or to boost the learning effect.
43, Reading Order Matters: Information Extraction from Visually-rich Documents By Token Path Prediction
Chong Zhang; Ya Guo; Yi Tu; Huan Chen; Jinyang Tang; Huijia Zhu; Qi Zhang; Tao Gui;
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Highlight: To address the reading order issue, we introduce Token Path Prediction (TPP), a simple prediction head to predict entity mentions as token sequences within documents.
44, Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements
Jiacheng Liu; Wenya Wang; Dianzhuo Wang; Noah Smith; Yejin Choi; Hannaneh Hajishirzi;
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Highlight: Today?s language models can be remarkably intelligent yet still produce text that contains trivial commonsense errors. Therefore, we seek a retrospective verification approach that can reflect on the commonsense plausibility of the machine text, and introduce Vera, a general-purpose model that learns to estimate the commonsense plausibility of declarative statements.
45, Exchange-of-Thought: Enhancing Large Language Model Capabilities Through Cross-Model Communication
Zhangyue Yin; Qiushi Sun; Cheng Chang; Qipeng Guo; Junqi Dai; Xuanjing Huang; Xipeng Qiu;
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Highlight: Despite this progress, their reasoning is often constrained by their intrinsic understanding, lacking external insights. To address this, we propose Exchange-of-Thought (EoT), a novel framework that enables cross-model communication during problem-solving.
46, Evaluating Object Hallucination in Large Vision-Language Models
Yifan Li; Yifan Du; Kun Zhou; Jinpeng Wang; Xin Zhao; Ji-Rong Wen;
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Highlight: To investigate it, this work presents the first systematic study on object hallucination of LVLMs.
47, Rethinking The Evaluation for Conversational Recommendation in The Era of Large Language Models
Xiaolei Wang; Xinyu Tang; Xin Zhao; Jingyuan Wang; Ji-Rong Wen;
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Highlight: In this paper, we embark on an investigation into the utilization of ChatGPT for CRSs, revealing the inadequacy of the existing evaluation protocol.
48, Meta-Learning Online Adaptation of Language Models
Nathan Hu; Eric Mitchell; Christopher Manning; Chelsea Finn;
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Highlight: That is, the gradient signal from important tokens representing factual information is drowned out by the gradient from inherently noisy tokens, suggesting that a dynamic, context-aware learning rate may be beneficial. We therefore propose learning which tokens to upweight.
49, HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models
Junyi Li; Xiaoxue Cheng; Xin Zhao; Jian-Yun Nie; Ji-Rong Wen;
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Highlight: To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the Hallucination Evaluation for Large Language Models (HaluEval) benchmark, a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination. To generate these samples, we propose a ChatGPT-based two-step framework, i. e. , sampling-then-filtering.
50, Enhancing Generative Retrieval with Reinforcement Learning from Relevance Feedback
Yujia Zhou; Zhicheng Dou; Ji-Rong Wen;
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Highlight: Nevertheless, this approach faces two fundamental challenges: (i) a discrepancy between the token-level probabilistic optimization and the broader document-level relevance estimation; (ii) an overemphasis on top-1 results at the expense of overall ranking quality. To tackle these challenges, we propose a generative retrieval model with reinforcement learning from relevance feedback, which aims to align token-level docid generation with document-level relevance estimation.
51, Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback
Katherine Tian; Eric Mitchell; Allan Zhou; Archit Sharma; Rafael Rafailov; Huaxiu Yao; Chelsea Finn; Christopher Manning;
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Highlight: However, the most widely-used LMs are fine-tuned with reinforcement learning from human feedback (RLHF-LMs), and some studies have suggested that RLHF-LMs produce conditional probabilities that are very poorly calibrated. In light of this perceived weakness, we conduct a broad evaluation of methods for extracting confidence scores from RLHF-LMs.
52, A Cheaper and Better Diffusion Language Model with Soft-Masked Noise
Jiaao Chen; Aston Zhang; Mu Li; Alex Smola; Diyi Yang;
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Highlight: For example, the generally used Gaussian noise can not handle the discrete corruption well, and the objectives in continuous spaces fail to be stable for textual data in the diffusion process especially when the dimension is high. To alleviate these issues, we introduce a novel diffusion model for language modeling, Masked-Diffuse LM, with lower training cost and better performances, inspired by linguistic features in languages.
53, Unlearn What You Want to Forget: Efficient Unlearning for LLMs
Jiaao Chen; Diyi Yang;
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Highlight: As a result, the ability to easily remove data related to individual users from such models while not deteriorating their predictive quality after the removal becomes increasingly important. To address these issues, in this work, we propose an efficient unlearning framework that could efficiently update LLMs without having to retrain the whole model after data removals, by introducing lightweight unlearning layers learned with a selective teacher-student objective into the transformers.
54, CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code
Shuyan Zhou; Uri Alon; Sumit Agarwal; Graham Neubig;
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Highlight: In this paper, we propose CodeBERTScore: an evaluation metric for code generation, which builds on BERTScore (Zhang et al. , 2020).
55, NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reports
Mael Jullien; Marco Valentino; Hannah Frost; Paul O?Regan; D?nal Landers; Andre Freitas;
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Highlight: In this work, we present a novel resource to advance research on NLI for reasoning on CTRs.
56, Instructed Language Models with Retrievers Are Powerful Entity Linkers
Zilin Xiao; Ming Gong; Jie Wu; Xingyao Zhang; Linjun Shou; Daxin Jiang;
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Highlight: Several methods of equipping language models with EL ability were proposed in this work, including (i) a sequence-to-sequence training EL objective with instruction-tuning, (ii) a novel generative EL framework based on a light-weight potential mention retriever that frees the model from heavy and non-parallelizable decoding, achieving 4? speedup without compromise on linking metrics.
57, Privacy Implications of Retrieval-Based Language Models
Yangsibo Huang; Samyak Gupta; Zexuan Zhong; Kai Li; Danqi Chen;
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Highlight: In this work, we present the first study of privacy risks in retrieval-based LMs, particularly kNN-LMs.
58, Enabling Large Language Models to Generate Text with Citations
Tianyu Gao; Howard Yen; Jiatong Yu; Danqi Chen;
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Highlight: In this work, our aim is to allow LLMs to generate text with citations, improving their factual correctness and verifiability.
59, Doolittle: Benchmarks and Corpora for Academic Writing Formalization
Shizhe Diao; Yongyu Lei; Liangming Pan; Tianqing Fang; Wangchunshu Zhou; Sedrick Keh; Min-Yen Kan; Tong Zhang;
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Highlight: We propose a more general task, Academic Writing Formalization (AWF), to improve the overall quality of formal academic writing at the paragraph level.
60, Collaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation
Wanrong Zhu; Xinyi Wang; Yujie Lu; Tsu-Jui Fu; Xin Wang; Miguel Eckstein; William Wang;
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Highlight: Despite the advancements of T2I models, a common issue encountered by users is the need for repetitive editing of input prompts in order to receive a satisfactory image, which is time-consuming and labor-intensive. Given the demonstrated text generation power of large-scale language models, such as GPT-k, we investigate the potential of utilizing such models to improve the prompt editing process for T2I generation.
61, Knowledge Rumination for Pre-trained Language Models
Yunzhi Yao; Peng Wang; Shengyu Mao; Chuanqi Tan; Fei Huang; Huajun Chen; Ningyu Zhang;
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Highlight: However, despite the promising outcome, we empirically observe that PLMs may have already encoded rich knowledge in their pre-trained parameters but fails to fully utilize them when applying to knowledge-intensive tasks. In this paper, we propose a new paradigm dubbed Knowledge Rumination to help the pre-trained language model utilize that related latent knowledge without retrieving them from the external corpus.
62, Editing Large Language Models: Problems, Methods, and Opportunities
Yunzhi Yao; Peng Wang; Bozhong Tian; Siyuan Cheng; Zhoubo Li; Shumin Deng; Huajun Chen; Ningyu Zhang;
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Highlight: Our objective is to provide valuable insights into the effectiveness and feasibility of each editing technique, thereby assisting the community in making informed decisions on the selection of the most appropriate method for a specific task or context.
63, Conceptor-Aided Debiasing of Large Language Models
Li Yifei; Lyle Ungar; Jo?o Sedoc;
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Highlight: We propose two methods of applying conceptors (1) bias subspace projection by post-processing by the conceptor NOT operation; and (2) a new architecture, conceptor-intervened BERT (CI-BERT), which explicitly incorporates the conceptor projection into all layers during training.
64, Sparse Low-rank Adaptation of Pre-trained Language Models
Ning Ding; Xingtai Lv; Qiaosen Wang; Yulin Chen; Bowen Zhou; Zhiyuan Liu; Maosong Sun;
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Highlight: Recognizing the need for more flexible adaptation, we extend the methodology of LoRA to an innovative approach we call sparse low-rank adaptation (SoRA) that enables dynamic adjustments to the intrinsic rank during the adaptation process.
65, Adapting Language Models to Compress Contexts
Alexis Chevalier; Alexander Wettig; Anirudh Ajith; Danqi Chen;
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Highlight: We propose to adapt pre-trained LMs into AutoCompressors.
66, We Are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields
Jan Philip Wahle; Terry Ruas; Mohamed Abdalla; Bela Gipp; Saif Mohammad;
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Highlight: In this paper, we quantify the degree of influence between 23 fields of study and NLP (on each other).
67, Universal Self-Adaptive Prompting
Xingchen Wan; Ruoxi Sun; Hootan Nakhost; Hanjun Dai; Julian Eisenschlos; Sercan Arik; Tomas Pfister;
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Highlight: However, while highly coveted and being the most general, zero-shot performances in LLMs are still typically weaker due to the lack of guidance and the difficulty of applying existing automatic prompt design methods in general tasks when ground-truth labels are unavailable. In this study, we address this by presenting Universal Self-Adaptive Prompting (USP), an automatic prompt design approach specifically tailored for zero-shot learning (while compatible with few-shot).
68, Syntactic Substitutability As Unsupervised Dependency Syntax
Jasper Jian; Siva Reddy;
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Highlight: Syntax is a latent hierarchical structure which underpins the robust and compositional nature of human language. In this work, we explore the hypothesis that syntactic dependencies can be represented in language model attention distributions and propose a new method to induce these structures theory-agnostically.
69, Composable Text Controls in Latent Space with ODEs
Guangyi Liu; Zeyu Feng; Yuan Gao; Zichao Yang; Xiaodan Liang; Junwei Bao; Xiaodong He; Shuguang Cui; Zhen Li; Zhiting Hu;
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Highlight: This paper proposes a new efficient approach for composable text operations in the compact latent space of text.
70, CoAnnotating: Uncertainty-Guided Work Allocation Between Human and Large Language Models for Data Annotation
Minzhi Li; Taiwei Shi; Caleb Ziems; Min-Yen Kan; Nancy Chen; Zhengyuan Liu; Diyi Yang;
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Highlight: We propose CoAnnotating, a novel paradigm for Human-LLM co-annotation of unstructured texts at scale.
71, Task-Agnostic Low-Rank Adapters for Unseen English Dialects
Zedian Xiao; William Held; Yanchen Liu; Diyi Yang;
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Highlight: However, prior work on dialect struggle with generalizing to evolving and emerging dialects in a scalable manner. To fill this gap, our method, HyperLoRA, leverages expert linguistic knowledge to enable resource-efficient adaptation via hypernetworks.
72, Impressions: Visual Semiotics and Aesthetic Impact Understanding
Julia Kruk; Caleb Ziems; Diyi Yang;
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Highlight: We present Impressions, a novel dataset through which to investigate the semiotics of images, and how specific visual features and design choices can elicit specific emotions, thoughts and beliefs.
73, DADA: Dialect Adaptation Via Dynamic Aggregation of Linguistic Rules
Yanchen Liu; William Held; Diyi Yang;
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Highlight: In this paper, we propose DADA (Dialect Adaptation via Dynamic Aggregation), a modular approach to imbue SAE-trained models with multi-dialectal robustness by composing adapters which handle specific linguistic features.
74, Language and Mental Health: Measures of Emotion Dynamics from Text As Linguistic Biosocial Markers
Daniela Teodorescu; Tiffany Cheng; Alona Fyshe; Saif Mohammad;
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Highlight: Here, for the first time, we study the relationship between tweet emotion dynamics and mental health disorders.
75, Contrastive Learning of Sentence Embeddings from Scratch
Junlei Zhang; Zhenzhong Lan; Junxian He;
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Highlight: due to copyright restrictions, data distribution issues, and messy formats, among other factors. To address these issues, we present SynCSE, a contrastive learning framework that trains sentence embeddings with synthetic data.
76, Specialist or Generalist? Instruction Tuning for Specific NLP Tasks
Chufan Shi; Yixuan Su; Cheng Yang; Yujiu Yang; Deng Cai;
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Highlight: In this paper, we investigate whether incorporating broadcoverage generalist instruction tuning can contribute to building a specialist model.
77, Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study
Boxin Wang; Wei Ping; Peng Xu; Lawrence McAfee; Zihan Liu; Mohammad Shoeybi; Yi Dong; Oleksii Kuchaiev; Bo Li; Chaowei Xiao; Anima Anandkumar; Bryan Catanzaro;
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Highlight: Thus, it is still an open question: shall we pretrain large autoregressive LMs with retrieval? To answer it, we perform a comprehensive study on a scalable pre-trained retrieval-augmented LM (i. e. , RETRO) compared with standard GPT and retrieval-augmented GPT incorporated at fine-tuning or inference stages.
78, Exploring The Impact of Model Scaling on Parameter-Efficient Tuning
Yusheng Su; Chi-Min Chan; Jiali Cheng; Yujia Qin; Yankai Lin; Shengding Hu; Zonghan Yang; Ning Ding; Xingzhi Sun; Guotong Xie; Zhiyuan Liu; Maosong Sun;
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Highlight: Hence, we hypothesize that model scaling mitigates the impact of design differences on PET methods. To investigate this hypothesis, we introduce a more flexible PET method called Arbitrary PET (APET) method.
79, Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and The Case of Information Extraction
Martin Josifoski; Marija Sakota; Maxime Peyrard; Robert West;
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Highlight: This work shows that useful data can be synthetically generated even for tasks that cannot be solved directly by LLMs: for problems with structured outputs, it is possible to prompt an LLM to perform the task in the reverse direction, by generating plausible input text for a target output structure.
80, Spoiler Detection As Semantic Text Matching
Ryan Tran; Canwen Xu; Julian McAuley;
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Highlight: This is primarily because the definition of a spoiler varies depending on the viewer?s progress in the show, and conventional spoiler detection methods lack the granularity to capture this complexity. To tackle this challenge, we propose the task of spoiler matching, which involves assigning an episode number to a spoiler given a specific TV show.
81, InterFair: Debiasing with Natural Language Feedback for Fair Interpretable Predictions
Bodhisattwa Majumder; Zexue He; Julian McAuley;
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Highlight: We explore two interactive setups with a frozen predictive model and show that users able to provide feedback can achieve a better and fairer balance between task performance and bias mitigation.
82, Editing Common Sense in Transformers
Anshita Gupta; Debanjan Mondal; Akshay Sheshadri; Wenlong Zhao; Xiang Li; Sarah Wiegreffe; Niket Tandon;
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Highlight: In this paper, we investigate whether commonsense judgments are causally associated with localized, editable parameters in Transformers, and we provide an affirmative answer.
83, Aligning Large Language Models Through Synthetic Feedback
Sungdong Kim; Sanghwan Bae; Jamin Shin; Soyoung Kang; Donghyun Kwak; Kang Yoo; Minjoon Seo;
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Highlight: In this work, we propose a novel alignment learning framework with synthetic feedback not dependent on extensive human annotations and proprietary LLMs.
84, Three Stream Based Multi-level Event Contrastive Learning for Text-Video Event Extraction
Jiaqi Li; Chuanyi Zhang; Miaozeng Du; Dehai Min; Yongrui Chen; Guilin Qi;
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Highlight: We observe that the same event triggers correspond to similar motion trajectories, which are hardly affected by the background noise. Moviated by this, we propose a Three Stream Multimodal Event Extraction framework (TSEE) that simultaneously utilizes the features of text sequence and video appearance, as well as the motion representations to enhance the event extraction capacity.
85, SOUL: Towards Sentiment and Opinion Understanding of Language
Yue Deng; Wenxuan Zhang; Sinno Pan; Lidong Bing;
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Highlight: However, despite the success of pre-trained language models in this area, they often fall short of capturing the broader complexities of sentiment analysis. To address this issue, we propose a new task called Sentiment and Opinion Understanding of Language (SOUL).
86, KNN-LM Does Not Improve Open-ended Text Generation
Shufan Wang; Yixiao Song; Andrew Drozdov; Aparna Garimella; Varun Manjunatha; Mohit Iyyer;
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Highlight: In this paper, we study the generation quality of interpolation-based retrieval-augmented language models (LMs).
87, The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models Via Chain-of-Thought Fine-Tuning
Seungone Kim; Se Joo; Doyoung Kim; Joel Jang; Seonghyeon Ye; Jamin Shin; Minjoon Seo;
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Highlight: In this work, we aim to equip smaller LMs with the step-by-step reasoning capability by instruction tuning with CoT rationales.
88, PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer
Lichang Chen; Jiuhai Chen; Heng Huang; Minhao Cheng;
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Highlight: We propose a new algorithm, called Prompt Tuning with Perturbation-based regularizer (PTP), which can not only alleviate training instability dramatically but also boost the performance of prompt tuning.
89, Explore-Instruct: Enhancing Domain-Specific Instruction Coverage Through Active Exploration
Fanqi Wan; Xinting Huang; Tao Yang; Xiaojun Quan; Wei Bi; Shuming Shi;
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Highlight: However, existing data employed for such tuning often exhibit an inadequate coverage of individual domains, limiting the scope for nuanced comprehension and interactions within these areas. To address this deficiency, we propose Explore-Instruct, a novel approach to enhance the data coverage to be used in domain-specific instruction-tuning through active exploration via Large Language Models (LLMs).
90, TheoremQA: A Theorem-driven Question Answering Dataset
Wenhu Chen; Ming Yin; Max Ku; Pan Lu; Yixin Wan; Xueguang Ma; Jianyu Xu; Xinyi Wang; Tony Xia;
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Highlight: In this paper, we introduce TheoremQA, the first theorem-driven question-answering dataset designed to evaluate AI models? capabilities to apply theorems to solve challenging science problems.
91, MemeCap: A Dataset for Captioning and Interpreting Memes
EunJeong Hwang; Vered Shwartz;
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Highlight: We present the task of meme captioning and release a new dataset, MemeCap.
92, Building Real-World Meeting Summarization Systems Using Large Language Models: A Practical Perspective
Md Tahmid Rahman Laskar; Xue-Yong Fu; Cheng Chen; Shashi Bhushan TN;
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Highlight: This paper studies how to effectively build meeting summarization systems for real-world usage using large language models (LLMs).
93, Character-LLM: A Trainable Agent for Role-Playing
Yunfan Shao; Linyang Li; Junqi Dai; Xipeng Qiu;
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Highlight: In this work, we introduce Character-LLM that teach LLMs to act as specific people such as Beethoven, Queen Cleopatra, Julius Caesar, etc.
94, Sparse Universal Transformer
Shawn Tan; Yikang Shen; Zhenfang Chen; Aaron Courville; Chuang Gan;
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Highlight: This is mainly because scaling UT parameters is more compute and memory intensive than scaling up a VT. This paper proposes the Sparse Universal Transformer (SUT), which leverages Sparse Mixture of Experts (SMoE) to reduce UT?s computation complexity while retaining its parameter efficiency and generalization ability.
95, Larger Probes Tell A Different Story: Extending Psycholinguistic Datasets Via In-Context Learning
Namrata Shivagunde; Vladislav Lialin; Anna Rumshisky;
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Highlight: In this work, we introduce new, larger datasets for negation (NEG-1500-SIMP) and role reversal (ROLE-1500) inspired by psycholinguistic studies.
96, RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation
Yue Zhang; Leyang Cui; Enbo Zhao; Wei Bi; Shuming Shi;
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Highlight: In this paper, we introduce RobustGEC, a benchmark designed to evaluate the context robustness of GEC systems.
97, Symbolic Planning and Code Generation for Grounded Dialogue
Justin Chiu; Wenting Zhao; Derek Chen; Saujas Vaduguru; Alexander Rush; Daniel Fried;
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Highlight: However, LLMs have had limited applicability in grounded task-oriented dialogue as they are difficult to steer toward task objectives and fail to handle novel grounding. We present a modular and interpretable grounded dialogue system that addresses these shortcomings by composing LLMs with a symbolic planner and grounded code execution.
98, Outlier Suppression+: Accurate Quantization of Large Language Models By Equivalent and Effective Shifting and Scaling
Xiuying Wei; Yunchen Zhang; Yuhang Li; Xiangguo Zhang; Ruihao Gong; Jinyang Guo; Xianglong Liu;
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Highlight: We observe that these outliers are concentrated in specific channels and are asymmetric across channels. To address this issue, we propose the Outlier Suppression+ (OS+) framework, which contains the channel-wise shifting for asymmetry and channel-wise scaling for concentration.
99, Controlling Pre-trained Language Models for Grade-Specific Text Simplification
Sweta Agrawal; Marine Carpuat;
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Highlight: In this work, we conduct an empirical study to understand how different control mechanisms impact the adequacy and simplicity of text simplification systems.
100, Do All Languages Cost The Same? Tokenization in The Era of Commercial Language Models
Orevaoghene Ahia; Sachin Kumar; Hila Gonen; Jungo Kasai; David Mortensen; Noah Smith; Yulia Tsvetkov;
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Highlight: What constitutes a token, however, is training data and model dependent with a large variance in the number of tokens required to convey the same information in different languages. In this work, we analyze the effect of this non-uniformity on the fairness of an API?s pricing policy across languages.
101, Evaluating Evaluation Metrics: A Framework for Analyzing NLG Evaluation Metrics Using Measurement Theory
Ziang Xiao; Susu Zhang; Vivian Lai; Q. Vera Liao;
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Highlight: Recognizing the limitations of existing automatic metrics and noises from how current human evaluation was conducted, we propose MetricEval, a framework informed by measurement theory, the foundation of educational test design, for conceptualizing and evaluating the reliability and validity of NLG evaluation metrics.
102, Improving Language Models? Meaning Understanding and Consistency By Learning Conceptual Roles from Dictionary
Myeongjun Jang; Thomas Lukasiewicz;
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Highlight: To this end, we propose a practical approach that alleviates the inconsistent behaviour issue by fundamentally improving PLMs? meaning awareness.
103, Consistency Analysis of ChatGPT
Myeongjun Jang; Thomas Lukasiewicz;
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Highlight: This paper investigates the trustworthiness of ChatGPT and GPT-4 regarding logically consistent behaviour, focusing specifically on semantic consistency and the properties of negation, symmetric, and transitive consistency.
104, Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs Without Fine-tuning
Ximing Lu; Faeze Brahman; Peter West; Jaehun Jung; Khyathi Chandu; Abhilasha Ravichander; Prithviraj Ammanabrolu; Liwei Jiang; Sahana Ramnath; Nouha Dziri; Jillian Fisher; Bill Lin; Skyler Hallinan; Lianhui Qin; Xiang Ren; Sean Welleck; Yejin Choi;
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Highlight: We propose Inference-time Policy Adapters (IPA), which efficiently tailors a language model such as GPT-3 without fine-tuning it.
105, KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection
Sehyun Choi; Tianqing Fang; Zhaowei Wang; Yangqiu Song;
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Highlight: Unfortunately, this method incurs high training costs and may cause catastrophic forgetting for multi-tasking models. To overcome these limitations, we propose a knowledge-constrained decoding method called KCTS (Knowledge-Constrained Tree Search), which guides a frozen LM to generate text aligned with the reference knowledge at each decoding step using a knowledge classifier score and MCTS (Monte-Carlo Tree Search).
106, Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination By Evaluation Benchmarks
Alon Jacovi; Avi Caciularu; Omer Goldman; Yoav Goldberg;
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Highlight: Assuming that all relevant actors value clean test data and will cooperate to mitigate data contamination, what can be done? We propose three strategies that can make a difference: (1) Test data made public should be encrypted with a public key and licensed to disallow derivative distribution; (2) demand training exclusion controls from closed API holders, and protect your test data by refusing to evaluate without them; (3) avoid data which appears with its solution on the internet, and release the web-page context of internet-derived data along with the data.
107, Focus Your Attention (with Adaptive IIR Filters)
Shahar Lutati; Itamar Zimerman; Lior Wolf;
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Highlight: We present a new layer in which dynamic (i. e. , input-dependent) Infinite Impulse Response (IIR) filters of order two are used to process the input sequence prior to applying conventional attention.
108, DetGPT: Detect What You Need Via Reasoning
Renjie Pi; Jiahui Gao; Shizhe Diao; Rui Pan; Hanze Dong; Jipeng Zhang; Lewei Yao; Jianhua Han; Hang Xu; Lingpeng Kong; Tong Zhang;
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Highlight: In this paper, we introduce a new paradigm for object detection that we call reasoning-based object detection.
109, Q2d: Turning Questions Into Dialogs to Teach Models How to Search
Yonatan Bitton; Shlomi Cohen-Ganor; Ido Hakimi; Yoad Lewenberg; Roee Aharoni; Enav Weinreb;
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Highlight: In this work, we propose q2d: an automatic data generation pipeline that generates information-seeking dialogs from questions.
110, ReTAG: Reasoning Aware Table to Analytic Text Generation
Deepanway Ghosal; Preksha Nema; Aravindan Raghuveer;
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Highlight: Through analysis of popular table to text benchmarks (ToTTo (Parikh et al. , 2020 and InfoTabs (Gupta et al. , 2020) we observe that in order to generate the ideal summary, multiple types of reasoning is needed coupled with access to knowledge beyond the scope of the table. To address this gap, we propose ReTAG, a table and reasoning aware model that uses vector-quantization to infuse different types of analytical reasoning into the output.
111, MoT: Memory-of-Thought Enables ChatGPT to Self-Improve
Xiaonan Li; Xipeng Qiu;
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Highlight: In this paper, we propose a framework, **MoT**, to let the LLM self-improve through **M**emory **o**f **T**houghts, without annotated datasets and parameter updates.
112, UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation
Daixuan Cheng; Shaohan Huang; Junyu Bi; Yuefeng Zhan; Jianfeng Liu; Yujing Wang; Hao Sun; Furu Wei; Weiwei Deng; Qi Zhang;
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Highlight: We propose UPRISE (Universal Prompt Retrieval for Improving zero-Shot Evaluation), which tunes a lightweight and versatile retriever that automatically retrieves prompts for a given zero-shot task input.
113, Do Transformers Parse While Predicting The Masked Word?
Haoyu Zhao; Abhishek Panigrahi; Rong Ge; Sanjeev Arora;
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Highlight: Some doubts have been raised whether the models are doing parsing or only some computation weakly correlated with it. Concretely: (a) Is it possible to explicitly describe transformers with realistic embedding dimensions, number of heads, etc. that are capable of doing parsing ? or even approximate parsing? (b) Why do pre-trained models capture parsing structure? This paper takes a step toward answering these questions in the context of generative modeling with PCFGs. We show that masked language models like BERT or RoBERTa of moderate sizes can approximately execute the Inside-Outside algorithm for the English PCFG (Marcus et al. , 1993).
114, SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization
Hyunwoo Kim; Jack Hessel; Liwei Jiang; Peter West; Ximing Lu; Youngjae Yu; Pei Zhou; Ronan Bras; Malihe Alikhani; Gunhee Kim; Maarten Sap; Yejin Choi;
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Highlight: Data scarcity has been a long standing issue in the field of open-domain social dialogue. To quench this thirst, we present SODA: the first publicly available, million-scale high-quality social dialogue dataset.
115, Explaining with Contrastive Phrasal Highlighting: A Case Study in Assisting Humans to Detect Translation Differences
Eleftheria Briakou; Navita Goyal; Marine Carpuat;
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Highlight: We introduce a technique to generate contrastive phrasal highlights that explain the predictions of a semantic divergence model via phrase alignment guided erasure.
116, APrompt: Attention Prompt Tuning for Efficient Adaptation of Pre-trained Language Models
Qifan Wang; Yuning Mao; Jingang Wang; Hanchao Yu; Shaoliang Nie; Sinong Wang; Fuli Feng; Lifu Huang; Xiaojun Quan; Zenglin Xu; Dongfang Liu;
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Highlight: In this work, we propose a novel Attention Prompt tuning method, namely APrompt, for efficient adaptation of pre-trained language models.
117, Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness?
Kevin Liu; Stephen Casper; Dylan Hadfield-Menell; Jacob Andreas;
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Highlight: We identify three different classes of disagreement, which we term confabulation, deception, and heterogeneity.
118, Can We Edit Multimodal Large Language Models?
Siyuan Cheng; Bozhong Tian; Qingbin Liu; Xi Chen; Yongheng Wang; Huajun Chen; Ningyu Zhang;
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Highlight: In this paper, we focus on editing multimodal Large Language Models (LLMs).
119, Active Instruction Tuning: Improving Cross-Task Generalization By Training on Prompt Sensitive Tasks
Po-Nien Kung; Fan Yin; Di Wu; Kai-Wei Chang; Nanyun Peng;
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Highlight: We discover that training on ambiguous (prompt-uncertain) tasks improves generalization while training on difficult (prompt-certain and low-probability) tasks offers no benefit, underscoring the importance of task selection for instruction tuning.
120, HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translation
David Dale; Elena Voita; Janice Lam; Prangthip Hansanti; Christophe Ropers; Elahe Kalbassi; Cynthia Gao; Loic Barrault; Marta Costa-juss?;
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Highlight: In this work, we release an annotated dataset for the hallucination and omission phenomena covering 18 translation directions with varying resource levels and scripts.
121, Oolong: Investigating What Makes Transfer Learning Hard with Controlled Studies
Zhengxuan Wu; Alex Tamkin; Isabel Papadimitriou;
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Highlight: To disentangle the impact of different factors like syntactic similarity and vocabulary similarity, we propose a set of controlled transfer studies: we systematically transform the language of the GLUE benchmark, altering one axis of crosslingual variation at a time, and then measure the resulting drops in a pretrained model?s downstream performance.
122, UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning
Ahmed Masry; Parsa Kavehzadeh; Do Long; Enamul Hoque; Shafiq Joty;
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Highlight: We propose several chart-specific pretraining tasks that include: (i) low-level tasks to extract the visual elements (e. g. , bars, lines) and data from charts, and (ii) high-level tasks to acquire chart understanding and reasoning skills.
123, Reformulating NLP Tasks to Capture Longitudinal Manifestation of Language Disorders in People with Dementia
Dimitris Gkoumas; Matthew Purver; Maria Liakata;
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Highlight: Here, we automatically learn linguistic disorder patterns by making use of a moderately-sized pre-trained language model and forcing it to focus on reformulated natural language processing (NLP) tasks and associated linguistic patterns.
124, A Digital Language Coherence Marker for Monitoring Dementia
Dimitris Gkoumas; Adam Tsakalidis; Maria Liakata;
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Highlight: Here we propose methods to capture language coherence as a cost-effective, human-interpretable digital marker for monitoring cognitive changes in people with dementia.
125, Ties Matter: Meta-Evaluating Modern Metrics with Pairwise Accuracy and Tie Calibration
Daniel Deutsch; George Foster; Markus Freitag;
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Highlight: We propose instead to meta-evaluate metrics with a version of pairwise accuracy that gives metrics credit for correctly predicting ties, in combination with a tie calibration procedure that automatically introduces ties into metric scores, enabling fair comparison between metrics that do and do not predict ties.
126, ReCEval: Evaluating Reasoning Chains Via Correctness and Informativeness
Archiki Prasad; Swarnadeep Saha; Xiang Zhou; Mohit Bansal;
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Highlight: Specifically, we propose ReCEval (Reasoning Chain Evaluation), a framework that evaluates reasoning chains via two key properties: (1) correctness, i. e. , each step makes a valid inference based on information contained within the step, preceding steps, and input context, and (2) informativeness, i. e. , each step provides new information that is helpful towards deriving the generated answer.
127, Skill-Based Few-Shot Selection for In-Context Learning
Shengnan An; Bo Zhou; Zeqi Lin; Qiang Fu; Bei Chen; Nanning Zheng; Weizhu Chen; Jian-Guang Lou;
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Highlight: In this paper, we propose **Skill-KNN**, a skill-based few-shot selection method for in-context learning.
128, IfQA: A Dataset for Open-domain Question Answering Under Counterfactual Presuppositions
Wenhao Yu; Meng Jiang; Peter Clark; Ashish Sabharwal;
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Highlight: Although counterfactual reasoning is a fundamental aspect of intelligence, the lack of large-scale counterfactual open-domain question-answering (QA) benchmarks makes it difficult to evaluate and improve models on this ability. To address this void, we introduce the first such dataset, named IfQA, where each question is based on a counterfactual presupposition via an ?if? clause.
129, Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting
Xi Ye; Greg Durrett;
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Highlight: This paper tackles the problem of how to optimize explanation-infused prompts in a blackbox fashion.
130, GlobalBench: A Benchmark for Global Progress in Natural Language Processing
Yueqi Song; Simran Khanuja; Pengfei Liu; Fahim Faisal; Alissa Ostapenko; Genta Winata; Alham Aji; Samuel Cahyawijaya; Yulia Tsvetkov; Antonios Anastasopoulos; Graham Neubig;
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Highlight: To track and further incentivize the global development of equitable language technology, we introduce GlobalBench.
131, UDAPDR: Unsupervised Domain Adaptation Via LLM Prompting and Distillation of Rerankers
Jon Saad-Falcon; Omar Khattab; Keshav Santhanam; Radu Florian; Martin Franz; Salim Roukos; Avirup Sil; Md Sultan; Christopher Potts;
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Highlight: However, such datasets are often unavailable, and their utility for real-world applications can diminish quickly due to domain shifts. To address this challenge, we develop and motivate a method for using large language models (LLMs) to generate large numbers of synthetic queries cheaply.
132, Byte Pair Encoding for Symbolic Music
Nathan Fradet; Nicolas Gutowski; Fabien Chhel; Jean-Pierre Briot;
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Highlight: In this paper, we show that Byte Pair Encoding, a compression technique widely used for natural language, significantly decreases the sequence length while increasing the vocabulary size.
133, Incorporating Structured Representations Into Pretrained Vision & Language Models Using Scene Graphs
Roei Herzig; Alon Mendelson; Leonid Karlinsky; Assaf Arbelle; Rogerio Feris; Trevor Darrell; Amir Globerson;
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Highlight: Here we ask whether small SG datasets can provide sufficient information for enhancing structured understanding of pretrained VLMs. We show that it is indeed possible to improve VLMs when learning from SGs by integrating components that incorporate structured information into both visual and textual representations.
134, Can We Edit Factual Knowledge By In-Context Learning?
Ce Zheng; Lei Li; Qingxiu Dong; Yuxuan Fan; Zhiyong Wu; Jingjing Xu; Baobao Chang;
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Highlight: Inspired by in-context learning (ICL), a new paradigm based on demonstration contexts without parameter updating, we explore whether ICL can edit factual knowledge.
135, Merging Experts Into One: Improving Computational Efficiency of Mixture of Experts
Shwai He; Run-Ze Fan; Liang Ding; Li Shen; Tianyi Zhou; Dacheng Tao;
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Highlight: Can we retain the advantages of adding more experts without substantially increasing the computational costs? In this paper, we first demonstrate the superiority of selecting multiple experts and then propose a computation-efficient approach called Merging Experts into One (MEO), which reduces the computation cost to that of a single expert.
136, SummEdits: Measuring LLM Ability at Factual Reasoning Through The Lens of Summarization
Philippe Laban; Wojciech Kryscinski; Divyansh Agarwal; Alexander Fabbri; Caiming Xiong; Shafiq Joty; Chien-Sheng Wu;
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Highlight: However, a closer analysis reveals issues with existing evaluation benchmarks, affecting evaluation precision. To address this, we propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits.
137, Mitigating Temporal Misalignment By Discarding Outdated Facts
Michael Zhang; Eunsol Choi;
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Highlight: To mitigate the effects of temporal misalignment, we propose fact duration prediction: the task of predicting how long a given fact will remain true.
138, Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models
Daman Arora; Himanshu Singh; Mausam;
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Highlight: In response, we present JEEBench, a considerably more challenging benchmark dataset for evaluating the problem solving abilities of LLMs.
139, Noisy Exemplars Make Large Language Models More Robust: A Domain-Agnostic Behavioral Analysis
Hongyi Zheng; Abulhair Saparov;
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Highlight: However, there is little existing work investigating the robustness of LLMs with few-shot prompting techniques. Therefore, we introduce a systematic approach to test the robustness of LLMs in multi-hop reasoning tasks via domain-agnostic perturbations.
140, Conversational Semantic Parsing Using Dynamic Context Graphs
Parag Jain; Mirella Lapata;
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Highlight: In this paper we consider the task of conversational semantic parsing over general purpose knowledge graphs (KGs) with millions of entities, and thousands of relation-types.
141, Enhancing Textbooks with Visuals from The Web for Improved Learning
Janvijay Singh; Vil?m Zouhar; Mrinmaya Sachan;
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Highlight: In this paper, we investigate the effectiveness of vision-language models to automatically enhance textbooks with images from the web.
142, Enhancing Biomedical Lay Summarisation with External Knowledge Graphs
Tomas Goldsack; Zhihao Zhang; Chen Tang; Carolina Scarton; Chenghua Lin;
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Highlight: Using both automatic and human evaluations, we systematically investigate the effectiveness of three different approaches for incorporating knowledge graphs within lay summarisation models, with each method targeting a distinct area of the encoder-decoder model architecture.
143, 3DRP-Net: 3D Relative Position-aware Network for 3D Visual Grounding
Zehan Wang; Haifeng Huang; Yang Zhao; Linjun Li; Xize Cheng; Yichen Zhu; Aoxiong Yin; Zhou Zhao;
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Highlight: In this work, we propose a relation-aware one-stage framework, named 3D Relative Position-aware Network (3DRP-Net), which can effectively capture the relative spatial relationships between objects and enhance object attributes.
144, Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation
Da Yin; Xiao Liu; Fan Yin; Ming Zhong; Hritik Bansal; Jiawei Han; Kai-Wei Chang;
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Highlight: In this paper, we propose Dynosaur, a dynamic growth paradigm for the automatic curation of instruction-tuning data.
145, MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter
Zhiyuan Liu; Sihang Li; Yanchen Luo; Hao Fei; Yixin Cao; Kenji Kawaguchi; Xiang Wang; Tat-Seng Chua;
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Highlight: However, they inherently lack 2D graph perception ? a critical ability of human professionals in comprehending molecules? topological structures. To bridge this gap, we propose MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter.
146, GD-COMET: A Geo-Diverse Commonsense Inference Model
Mehar Bhatia; Vered Shwartz;
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Highlight: In this paper, we present GD-COMET, a geo-diverse version of the COMET commonsense inference model.
147, Crossing The Threshold: Idiomatic Machine Translation Through Retrieval Augmentation and Loss Weighting
Emmy Liu; Aditi Chaudhary; Graham Neubig;
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Highlight: To improve translation of natural idioms, we introduce two straightforward yet effective techniques: the strategic upweighting of training loss on potentially idiomatic sentences, and using retrieval-augmented models.
148, Homophone Disambiguation Reveals Patterns of Context Mixing in Speech Transformers
Hosein Mohebbi; Grzegorz Chrupala; Willem Zuidema; Afra Alishahi;
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Highlight: Transformers have become a key architecture in speech processing, but our understanding of how they build up representations of acoustic and linguistic structure is limited. In this study, we address this gap by investigating how measures of ?context-mixing? developed for text models can be adapted and applied to models of spoken language.
149, Non-autoregressive Streaming Transformer for Simultaneous Translation
Zhengrui Ma; Shaolei Zhang; Shoutao Guo; Chenze Shao; Min Zhang; Yang Feng;
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Highlight: To address those issues, we propose non-autoregressive streaming Transformer (NAST) which comprises a unidirectional encoder and a non-autoregressive decoder with intra-chunk parallelism.
150, SeqXGPT: Sentence-Level AI-Generated Text Detection
Pengyu Wang; Linyang Li; Ke Ren; Botian Jiang; Dong Zhang; Xipeng Qiu;
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Highlight: These features are composed like waves in speech processing and cannot be studied by LLMs. Therefore, we build SeqXGPT based on convolution and self-attention networks.
151, Knowledge-Augmented Language Model Verification
Jinheon Baek; Soyeong Jeong; Minki Kang; Jong Park; Sung Hwang;
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Highlight: To overcome these, we propose to verify the output and the knowledge of the knowledge-augmented LMs with a separate verifier, which is a small LM that is trained to detect those two types of errors through instruction-finetuning.
152, Explicit Planning Helps Language Models in Logical Reasoning
Hongyu Zhao; Kangrui Wang; Mo Yu; Hongyuan Mei;
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Highlight: In this paper, we propose LEAP, a novel system that uses language models to perform multi-step logical reasoning and incorporates explicit planning into the inference procedure.
153, API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
Minghao Li; Yingxiu Zhao; Bowen Yu; Feifan Song; Hangyu Li; Haiyang Yu; Zhoujun Li; Fei Huang; Yongbin Li;
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Highlight: However, three pivotal questions remain unanswered: (1) How effective are current LLMs in utilizing tools? (2) How can we enhance LLMs? ability to utilize tools? (3) What obstacles need to be overcome to leverage tools? To address these questions, we introduce API-Bank, a groundbreaking benchmark, specifically designed for tool-augmented LLMs.
154, Towards Interpretable Mental Health Analysis with Large Language Models
Kailai Yang; Shaoxiong Ji; Tianlin Zhang; Qianqian Xie; Ziyan Kuang; Sophia Ananiadou;
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Highlight: However, existing relevant studies bear several limitations, including inadequate evaluations, lack of prompting strategies, and ignorance of exploring LLMs for explainability. To bridge these gaps, we comprehensively evaluate the mental health analysis and emotional reasoning ability of LLMs on 11 datasets across 5 tasks.
155, Label Words Are Anchors: An Information Flow Perspective for Understanding In-Context Learning
Lean Wang; Lei Li; Damai Dai; Deli Chen; Hao Zhou; Fandong Meng; Jie Zhou; Xu Sun;
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Highlight: In this paper, we investigate the working mechanism of ICL through an information flow lens.
156, A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents
Benjamin Newman; Luca Soldaini; Raymond Fok; Arman Cohan; Kyle Lo;
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Highlight: In this work, we use language models to rewrite snippets from scientific documents to be read on their own.
157, Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4
Kent Chang; Mackenzie Cramer; Sandeep Soni; David Bamman;
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Highlight: In this work, we carry out a data archaeology to infer books that are known to ChatGPT and GPT-4 using a name cloze membership inference query.
158, Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Media
Shubham Mittal; Megha Sundriyal; Preslav Nakov;
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Highlight: Despite its importance to journalists and human fact-checkers, it remains a severely understudied problem, and the scarce research on this topic so far has only focused on English. Here we aim to bridge this gap by creating a novel dataset, X-CLAIM, consisting of 7K real-world claims collected from numerous social media platforms in five Indian languages and English.
159, Detecting Propaganda Techniques in Code-Switched Social Media Text
Muhammad Salman; Asif Hanif; Shady Shehata; Preslav Nakov;
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Highlight: Code-switching combines different languages within the same text, which poses a challenge for automatic systems. Considering this premise, we propose a novel task of detecting propaganda techniques in code-switched text.
160, LLM-powered Data Augmentation for Enhanced Cross-lingual Performance
Chenxi Whitehouse; Monojit Choudhury; Alham Aji;
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Highlight: This paper explores the potential of leveraging Large Language Models (LLMs) for data augmentation in multilingual commonsense reasoning datasets where the available training data is extremely limited.
161, On The Challenges of Using Black-Box APIs for Toxicity Evaluation in Research
Luiza Pozzobon; Beyza Ermis; Patrick Lewis; Sara Hooker;
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Highlight: Our findings suggest that research that relied on inherited automatic toxicity scores to compare models and techniques may have resulted in inaccurate findings.
162, Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization
Tianshi Che; Ji Liu; Yang Zhou; Jiaxiang Ren; Jiwen Zhou; Victor Sheng; Huaiyu Dai; Dejing Dou;
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Highlight: This paper proposes a Parameter-efficient prompt Tuning approach with Adaptive Optimization, i. e. , FedPepTAO, to enable efficient and effective FL of LLMs.
163, Appraising The Potential Uses and Harms of LLMs for Medical Systematic Reviews
Hye Yun; Iain Marshall; Thomas Trikalinos; Byron Wallace;
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Highlight: We conducted 16 interviews with international systematic review experts to characterize the perceived utility and risks of LLMs in the specific context of medical evidence reviews.
164, Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU
Fajri Koto; Nurul Aisyah; Haonan Li; Timothy Baldwin;
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Highlight: In this work, we introduce IndoMMLU, the first multi-task language understanding benchmark for Indonesian culture and languages, which consists of questions from primary school to university entrance exams in Indonesia.
165, IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions
Ziheng Zeng; Kellen Cheng; Srihari Nanniyur; Jianing Zhou; Suma Bhat;
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Highlight: Unlike prior works that enable IE comprehension through fine-tuning PTLMs with sentences containing IEs, in this work, we construct IEKG, a commonsense knowledge graph for figurative interpretations of IEs.
166, Generating Data for Symbolic Language with Large Language Models
Jiacheng Ye; Chengzu Li; Lingpeng Kong; Tao Yu;
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Highlight: In this paper, we propose SymGen which utilizes LLMs for generating various annotation-expensive symbolic language data.
167, Text Encoders Bottleneck Compositionality in Contrastive Vision-language Models
Amita Kamath; Jack Hessel; Kai-Wei Chang;
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Highlight: We first curate CompPrompts, a set of increasingly compositional image captions that VL models should be able to capture (e. g. , single object, to object+property, to multiple interacting objects). Then, we train text-only recovery probes that aim to reconstruct captions from single-vector text representations produced by several VL models.
168, What?s ?up? with Vision-language Models? Investigating Their Struggle with Spatial Reasoning
Amita Kamath; Jack Hessel; Kai-Wei Chang;
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Highlight: We evaluate 18 VL models, finding that all perform poorly, e. g. , BLIP finetuned on VQAv2, which nears human parity on VQAv2, achieves 56% accuracy on our benchmarks vs. humans at 99%.
169, An Integrated Search System for Korea Weather Data
Jinkyung Jo; Dayeon Ki; Soyoung Yoon; Minjoon Seo;
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Highlight: We introduce WeatherSearch, an integrated search system deployed at the Korea Meteorological Administration (KMA).
170, Guideline Learning for In-Context Information Extraction
Chaoxu Pang; Yixuan Cao; Qiang Ding; Ping Luo;
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Highlight: In this paper, we propose a Guideline Learning (GL) framework for In-context IE which reflectively learns and follows guidelines.
171, RainProof: An Umbrella to Shield Text Generator from Out-Of-Distribution Data
Maxime Darrin; Pablo Piantanida; Pierre Colombo;
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Highlight: In this work, we focus on leveraging soft-probabilities in a black-box framework, i. e. we can access the soft-predictions but not the internal states of the model.
172, Continually Improving Extractive QA Via Human Feedback
Ge Gao; Hung-Ting Chen; Yoav Artzi; Eunsol Choi;
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Highlight: We study continually improving an extractive question answering (QA) system via human user feedback.
173, BERTie Bott?s Every Flavor Labels: A Tasty Introduction to Semantic Role Labeling for Galician
Micaella Bruton; Meriem Beloucif;
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Highlight: In this paper, we leverage existing corpora, WordNet, and dependency parsing to build the first Galician dataset for training semantic role labeling systems in an effort to expand available NLP resources.
174, Clembench: Using Game Play to Evaluate Chat-Optimized Language Models As Conversational Agents
Kranti Chalamalasetti; Jana G?tze; Sherzod Hakimov; Brielen Madureira; Philipp Sadler; David Schlangen;
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Highlight: As a proof of concept, this paper investigates five interaction settings, showing that current chat-optimised LLMs are, to an extent, capable of following game-play instructions.
175, Distance-Based Propagation for Efficient Knowledge Graph Reasoning
Harry Shomer; Yao Ma; Juanhui Li; Bo Wu; Charu Aggarwal; Jiliang Tang;
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Highlight: Though there are a few recent attempts to address this through learnable path pruning, they often sacrifice the performance to gain efficiency. In this work, we identify two intrinsic limitations of these methods that affect the efficiency and representation quality.
176, Multilingual Pixel Representations for Translation and Effective Cross-lingual Transfer
Elizabeth Salesky; Neha Verma; Philipp Koehn; Matt Post;
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Highlight: We introduce and demonstrate how to effectively train multilingual machine translation models with pixel representations.
177, Hybrid Inverted Index Is A Robust Accelerator for Dense Retrieval
Peitian Zhang; Zheng Liu; Shitao Xiao; Zhicheng Dou; Jing Yao;
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Highlight: In this work, we present the Hybrid Inverted Index (HI2), where the embedding clusters and salient terms work collaboratively to accelerate dense retrieval.
178, Harnessing LLMs for Temporal Data - A Study on Explainable Financial Time Series Forecasting
Xinli Yu; Zheng Chen; Yanbin Lu;
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Highlight: The study demonstrates LLMs? ability to generate well-reasoned decisions by leveraging cross-sequence information and extracting insights from text and price time series.
179, MAggretriever: A Simple Yet Effective Approach to Zero-Shot Multilingual Dense Retrieval
Sheng-Chieh Lin; Amin Ahmad; Jimmy Lin;
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Highlight: In this paper, we introduce mAggretriever, which effectively leverages semantic and lexical features from pre-trained multilingual transformers (e. g. , mBERT and XLM-R) for dense retrieval.
180, Indicative Summarization of Long Discussions
Shahbaz Syed; Dominik Schwabe; Khalid Khatib; Martin Potthast;
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Highlight: This paper presents a novel unsupervised approach using large language models (LLMs) to generating indicative summaries for long discussions that basically serve as tables of contents.
181, A Training-Free Debiasing Framework with Counterfactual Reasoning for Conversational Emotion Detection
Geng Tu; Ran Jing; Bin Liang; Min Yang; Kam-Fai Wong; Ruifeng Xu;
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Highlight: However, previous studies in ERC generally focus on capturing context-sensitive and speaker-sensitive dependencies, ignoring the unintended dataset biases of data, which hampers the generalization and fairness in ERC. To address this issue, we propose a Training-Free Debiasing framework (TFD) that operates during prediction without additional training.
182, A Mechanistic Interpretation of Arithmetic Reasoning in Language Models Using Causal Mediation Analysis
Alessandro Stolfo; Yonatan Belinkov; Mrinmaya Sachan;
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Highlight: Mathematical reasoning in large language models (LMs) has garnered significant attention in recent work, but there is a limited understanding of how these models process and store information related to arithmetic tasks within their architecture. In order to improve our understanding of this aspect of language models, we present a mechanistic interpretation of Transformer-based LMs on arithmetic questions using a causal mediation analysis framework.
183, Let?s Sample Step By Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs
Pranjal Aggarwal; Aman Madaan; Yiming Yang; Mausam;
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Highlight: In response, we introduce Adaptive-Consistency, a cost-efficient, model-agnostic technique that dynamically adjusts the number of samples per question using a lightweight stopping criterion.
184, Enhancing Chat Language Models By Scaling High-quality Instructional Conversations
Ning Ding; Yulin Chen; Bokai Xu; Yujia Qin; Shengding Hu; Zhiyuan Liu; Maosong Sun; Bowen Zhou;
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Highlight: This paper aims to push the upper bound of open-source models further.
185, Knowledge Graph Compression Enhances Diverse Commonsense Generation
EunJeong Hwang; Veronika Thost; Vered Shwartz; Tengfei Ma;
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Highlight: However, due to the large coverage and, consequently, vast scale of ConceptNet, the extracted subgraphs may contain loosely related, redundant and irrelevant information, which can introduce noise into the model. We propose to address this by applying a differentiable graph compression algorithm that focuses on the relevant knowledge for the task.
186, Decoding The Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting
Chenkai Sun; Jinning Li; Yi Fung; Hou Chan; Tarek Abdelzaher; ChengXiang Zhai; Heng Ji;
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Highlight: However, existing approaches have limited exploration of how to best process and utilize these important features. To address this gap, we propose a novel framework, named SocialSense, that leverages a large language model to induce a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics.
187, Non-Programmers Can Label Programs Indirectly Via Active Examples: A Case Study with Text-to-SQL
Ruiqi Zhong; Charlie Snell; Dan Klein; Jason Eisner;
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Highlight: We introduce APEL, a framework in which non-programmers select among candidate programs generated by a seed semantic parser (e. g. , Codex).
188, Goal-Driven Explainable Clustering Via Language Descriptions
Zihan Wang; Jingbo Shang; Ruiqi Zhong;
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Highlight: We propose a new task formulation, ?Goal-Driven Clustering with Explanations? (GoalEx), which represents both the goal and the explanations as free-form language descriptions.
189, Grammar-Constrained Decoding for Structured NLP Tasks Without Finetuning
Saibo Geng; Martin Josifoski; Maxime Peyrard; Robert West;
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Highlight: In this work, we demonstrate that formal grammars can describe the output space for a much wider range of tasks and argue that GCD can serve as a unified framework for structured NLP tasks in general.
190, Does The Correctness of Factual Knowledge Matter for Factual Knowledge-Enhanced Pre-trained Language Models?
Boxi Cao; Qiaoyu Tang; Hongyu Lin; Xianpei Han; Le Sun;
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Highlight: In this paper, we introduce a counterfactual-based analysis framework to explore the causal effects of factual knowledge injection on the performance of language models within pretrain-finetune paradigm.
191, When Language Models Fall in Love: Animacy Processing in Transformer Language Models
Michael Hanna; Yonatan Belinkov; Sandro Pezzelle;
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Highlight: Like previous studies, we find that LMs behave much like humans when presented with entities whose animacy is typical. However, we also show that even when presented with stories about atypically animate entities, such as a peanut in love, LMs adapt: they treat these entities as animate, though they do not adapt as well as humans.
192, Accelerating Toeplitz Neural Network with Constant-time Inference Complexity
Zhen Qin; Yiran Zhong;
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Highlight: In this paper, we aim to combine the strengths of TNNs and SSMs by converting TNNs to SSMs during inference, thereby enabling TNNs to achieve the same constant inference complexities as SSMs.
193, Mirages. On Anthropomorphism in Dialogue Systems
Gavin Abercrombie; Amanda Curry; Tanvi Dinkar; Verena Rieser; Zeerak Talat;
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Highlight: In this paper, we discuss the linguistic factors that contribute to the anthropomorphism of dialogue systems and the harms that can arise thereof, including reinforcing gender stereotypes and conceptions of acceptable language.
194, PK-ICR: Persona-Knowledge Interactive Multi-Context Retrieval for Grounded Dialogue
Minsik Oh; Joosung Lee; Jiwei Li; Guoyin Wang;
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Highlight: We develop a novel grounding retrieval method that utilizes all contexts of dialogue simultaneously.
195, Did You Mean...? Confidence-based Trade-offs in Semantic Parsing
Elias Stengel-Eskin; Benjamin Van Durme;
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Highlight: We propose the DidYouMean system which better balances usability and safety by rephrasing low-confidence inputs.
196, STAIR: Learning Sparse Text and Image Representation in Grounded Tokens
Chen Chen; Bowen Zhang; Liangliang Cao; Jiguang Shen; Tom Gunter; Albin Jose; Alexander Toshev; Yantao Zheng; Jonathon Shlens; Ruoming Pang; Yinfei Yang;
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Highlight: In this work, we show that it is possible to build a sparse semantic representation that is as powerful as, or even better than, dense presentations.
197, Where to Start? Analyzing The Potential Value of Intermediate Models
Leshem Choshen; Elad Venezian; Shachar Don-Yehiya; Noam Slonim; Yoav Katz;
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Highlight: Such a model, finetuned on some source dataset, may provide a better starting point for a new finetuning process on a desired target dataset. Here, we perform a systematic analysis of this intertraining scheme, over a wide range of English classification tasks.
198, INFORM : Information ENtropy Based Multi-step Reasoning FOR Large Language Models
Chuyue Zhou; Wangjie You; Juntao Li; Jing Ye; Kehai Chen; Min Zhang;
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Highlight: In this work, we propose a novel approach by introducing information entropy (IE) as a criteria on for CoT prompt selection.
199, DecipherPref: Analyzing Influential Factors in Human Preference Judgments Via GPT-4
Yebowen Hu; Kaiqiang Song; Sangwoo Cho; Xiaoyang Wang; Hassan Foroosh; Fei Liu;
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Highlight: In this paper, we conduct an in-depth examination of a collection of pairwise human judgments released by OpenAI.
200, Rethinking Negative Pairs in Code Search
Haochen Li; Xin Zhou; Anh Luu; Chunyan Miao;
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Highlight: As an example, a bubble sorting algorithm example is less ?negative? than a file saving function for the quick sorting algorithm query. In this paper, we tackle the above problems by proposing a simple yet effective Soft-InfoNCE loss that inserts weight terms into InfoNCE.
201, ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer
Huadai Liu; Rongjie Huang; Xuan Lin; Wenqiang Xu; Maozong Zheng; Hong Chen; Jinzheng He; Zhou Zhao;
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Highlight: In this work, we propose ViT-TTS, the first visual TTS model with scalable diffusion transformers.
202, Evaluating Cross-Domain Text-to-SQL Models and Benchmarks
Mohammadreza Pourreza; Davood Rafiei;
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Highlight: However, accurately matching a model-generated SQL query to a reference SQL query in a benchmark fails for various reasons, such as underspecified natural language queries, inherent assumptions in both model-generated and reference queries, and the non-deterministic nature of SQL output under certain conditions. In this paper, we conduct an extensive study of several prominent cross-domain text-to-SQL benchmarks and re-evaluate some of the top-performing models within these benchmarks, by both manually evaluating the SQL queries and rewriting them in equivalent expressions.
203, Robust Prompt Optimization for Large Language Models Against Distribution Shifts
Moxin Li; Wenjie Wang; Fuli Feng; Yixin Cao; Jizhi Zhang; Tat-Seng Chua;
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Highlight: In this light, we propose a new problem of robust prompt optimization for LLMs against distribution shifts, which requires the prompt optimized over the labeled source group can simultaneously generalize to an unlabeled target group. To solve this problem, we propose Generalized Prompt Optimization framework , which incorporates the unlabeled data from the target group into prompt optimization.
204, GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding
Zekun Li; Wenxuan Zhou; Yao-Yi Chiang; Muhao Chen;
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Highlight: This paper introduces GeoLM, a geospatially grounded language model that enhances the understanding of geo-entities in natural language.
205, Can LLMs Facilitate Interpretation of Pre-trained Language Models?
Basel Mousi; Nadir Durrani; Fahim Dalvi;
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Highlight: We propose using a large language model, ChatGPT, as an annotator to enable fine-grained interpretation analysis of pre-trained language models.
206, Enhancing Code-Switching for Cross-lingual SLU: A Unified View of Semantic and Grammatical Coherence
Zhihong Zhu; Xuxin Cheng; Zhiqi Huang; Dongsheng Chen; Yuexian Zou;
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Highlight: We ascribe this lack to two issues: (1) randomly replacing code-switched tokens with equal probability and (2) disregarding token-level dependency within each language. To tackle these issues, in this paper, we propose a novel method termed SoGo, for zero-shot cross-lingual SLU.
207, Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with The GeNTE Corpus
Andrea Piergentili; Beatrice Savoldi; Dennis Fucci; Matteo Negri; Luisa Bentivogli;
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Highlight: Based on GeNTE, we then overview existing reference-based evaluation approaches, highlight their limits, and propose a reference-free method more suitable to assess gender-neutral translation.
208, Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence Models
Di Wu; Wasi Ahmad; Kai-Wei Chang;
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Highlight: Regarding decoding, we demonstrate that while greedy search achieves strong F1 scores, it lags in recall compared with sampling-based methods. Based on these insights, we propose DeSel, a likelihood-based decode-select algorithm for seq2seq PLMs.
209, HistAlign: Improving Context Dependency in Language Generation By Aligning with History
David Wan; Shiyue Zhang; Mohit Bansal;
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Highlight: However, we find that even with training, the performance gain stemming from the cache component of current cache-LMs is suboptimal due to the misalignment between the current hidden states and those stored in the memory. In this work, we present HistAlign, a new training approach to ensure good cache alignment such that the model receives useful signals from the history.
210, Data Factors for Better Compositional Generalization
Xiang Zhou; Yichen Jiang; Mohit Bansal;
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Highlight: However, in contrast to this poor performance, state-of-the-art models trained on larger and more general datasets show better generalization ability. In this work, to reconcile this inconsistency, we conduct an empirical analysis by training Transformer models on a variety of training sets with different data factors, including dataset scale, pattern complexity, example difficulty, etc.
211, Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models
Jirui Qi; Raquel Fern?ndez; Arianna Bisazza;
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Highlight: With the ultimate goal of ensuring that users with different language backgrounds obtain consistent feedback from the same model, we study the cross-lingual consistency (CLC) of factual knowledge in various multilingual PLMs.
212, Do LLMs Understand Social Knowledge? Evaluating The Sociability of Large Language Models with SocKET Benchmark
Minje Choi; Jiaxin Pei; Sagar Kumar; Chang Shu; David Jurgens;
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Highlight: Here, we introduce a new theory-driven benchmark, SocKET, that contains 58 NLP tasks testing social knowledge which we group into five categories: humor & sarcasm, offensiveness, sentiment & emotion, and trustworthiness.
213, Improved Pseudo Data for Machine Translation Quality Estimation with Constrained Beam Search
Xiang Geng; Yu Zhang; Zhejian Lai; Shuaijie She; Wei Zou; Shimin Tao; Hao Yang; Jiajun Chen; Shujian Huang;
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Highlight: However, pseudo data solutions are less satisfying in unsupervised scenarios because the pseudo labels are inaccurate or the pseudo translations differ from the real ones. To address these problems, we propose to generate pseudo data using the MT model with constrained beam search (CBSQE).
214, IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems
Xu Huang; Zhirui Zhang; Ruize Gao; Yichao Du; Lemao Liu; Guoping Huang; Shuming Shi; Jiajun Chen; Shujian Huang;
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Highlight: We present IMTLab, an open-source end-to-end interactive machine translation (IMT) system platform that enables researchers to quickly build IMT systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems.
215, Beyond Factuality: A Comprehensive Evaluation of Large Language Models As Knowledge Generators
Liang Chen; Yang Deng; Yatao Bian; Zeyu Qin; Bingzhe Wu; Tat-Seng Chua; Kam-Fai Wong;
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Highlight: However, community concerns abound regarding the factuality and potential implications of using this uncensored knowledge. In light of this, we introduce CONNER, a COmpreheNsive kNowledge Evaluation fRamework, designed to systematically and automatically evaluate generated knowledge from six important perspectives ? Factuality, Relevance, Coherence, Informativeness, Helpfulness and Validity.
216, Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification
Liam Cripwell; Jo?l Legrand; Claire Gardent;
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Highlight: We propose a new learned evaluation metric ? SLE ? which focuses on simplicity, outperforming almost all existing metrics in terms of correlation with human judgements.
217, The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models
Jingyuan Qi; Zhiyang Xu; Ying Shen; Minqian Liu; Di Jin; Qifan Wang; Lifu Huang;
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Highlight: Inspired by the human cognitive process, we propose SOCRATIC QUESTIONING, a divide-and-conquer style algorithm that mimics the recursive thinking process.
218, Towards A Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models
Yifan Hou; Jiaoda Li; Yu Fei; Alessandro Stolfo; Wangchunshu Zhou; Guangtao Zeng; Antoine Bosselut; Mrinmaya Sachan;
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Highlight: However, it is unclear whether LMs perform these tasks by cheating with answers memorized from pretraining corpus, or, via a multi-step reasoning mechanism. In this paper, we try to answer this question by exploring a mechanistic interpretation of LMs for multi-step reasoning tasks.
219, A Diachronic Perspective on User Trust in AI Under Uncertainty
Shehzaad Dhuliawala; Vil?m Zouhar; Mennatallah El-Assady; Mrinmaya Sachan;
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Highlight: However, modern NLP systems are seldom calibrated and are often confidently incorrect about their predictions, which violates users? mental model and erodes their trust. In this work, we design a study where users bet on the correctness of an NLP system, and use it to study the evolution of user trust as a response to these trust-eroding events and how the user trust is rebuilt as a function of time after these events.
220, Towards Robust Pruning: An Adaptive Knowledge-Retention Pruning Strategy for Language Models
Jianwei Li; Qi Lei; Wei Cheng; Dongkuan Xu;
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Highlight: As humans step into the era of large language models, these issues become increasingly prominent. This paper proposes that the robustness of language models is proportional to the extent of pre-trained knowledge they encompass.
221, From Wrong To Right: A Recursive Approach Towards Vision-Language Explanation
Jiaxin Ge; Sanjay Subramanian; Trevor Darrell; Boyi Li;
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Highlight: Addressing the challenge of adapting pre-trained vision-language models for generating insightful explanations for visual reasoning tasks with limited annotations, we present ReVisE: a Recursive Visual Explanation algorithm.
222, Increasing Probability Mass on Answer Choices Does Not Always Improve Accuracy
Sarah Wiegreffe; Matthew Finlayson; Oyvind Tafjord; Peter Clark; Ashish Sabharwal;
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Highlight: Are there direct ways of reducing it, and does doing so improve task performance? We propose a mathematical formalism for SFC which allows us to quantify and bound its impact for the first time.
223, INSTRUCTSCORE: Towards Explainable Text Generation Evaluation with Automatic Feedback
Wenda Xu; Danqing Wang; Liangming Pan; Zhenqiao Song; Markus Freitag; William Wang; Lei Li;
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Highlight: Although recent learned metrics show high correlation with human judgement, these metrics do not provide explicit explanation of their verdict, nor associate the scores with defects in the generated text. To address this limitation, we present INSTRUCTSCORE, a fine-grained explainable evaluation metric for text generation.
224, Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Models
Laura Cabello; Emanuele Bugliarello; Stephanie Brandl; Desmond Elliott;
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Highlight: In this work, we define gender bias as our case study.
225, ?Mistakes Help Us Grow?: Facilitating and Evaluating Growth Mindset Supportive Language in Classrooms
Kunal Handa; Margarett Clapper; Jessica Boyle; Rose Wang; Diyi Yang; David Yeager; Dorottya Demszky;
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Highlight: We explore whether large language models (LLMs) can provide automated, personalized coaching to support teachers? use of GMSL.
226, Model-tuning Via Prompts Makes NLP Models Adversarially Robust
Mrigank Raman; Pratyush Maini; J Kolter; Zachary Lipton; Danish Pruthi;
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Highlight: In this work, we demonstrate surprising gains in adversarial robustness enjoyed by Model-tuning Via Prompts (MVP), an alternative method of adapting to downstream tasks.
227, Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation
Jiaang Li; Quan Wang; Yi Liu; Licheng Zhang; Zhendong Mao;
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Highlight: We refer to the process of obtaining corresponding codewords of each entity as entity quantization, for which previous works have designed complicated strategies. Surprisingly, this paper shows that simple random entity quantization can achieve similar results to current strategies.
228, Understanding The Inner-workings of Language Models Through Representation Dissimilarity
Davis Brown; Charles Godfrey; Nicholas Konz; Jonathan Tu; Henry Kvinge;
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Highlight: In this work we show that representation dissimilarity measures, which are functions that measure the extent to which two model?s internal representations differ, can be a valuable tool for gaining insight into the mechanics of language models.
229, Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks
Haoqi Zheng; Qihuang Zhong; Liang Ding; Zhiliang Tian; Xin Niu; Changjian Wang; Dongsheng Li; Dacheng Tao;
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Highlight: In this paper, we propose a self-evolution learning (SE) based mixup approach for data augmentation in text classification, which can generate more adaptive and model-friendly pseudo samples for the model training.
230, Zero-shot Sharpness-Aware Quantization for Pre-trained Language Models
Miaoxi Zhu; Qihuang Zhong; Li Shen; Liang Ding; Juhua Liu; Bo Du; Dacheng Tao;
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Highlight: Most of the cutting-edge zero-shot quantization methods primarily 1) apply to computer vision tasks, and 2) neglect of overfitting problem in the generative adversarial learning process, leading to sub-optimal performance. Motivated by this, we propose a novel zero-shot sharpness-aware quantization (ZSAQ) framework for the zero-shot quantization of various PLMs.
231, NL2TL: Transforming Natural Languages to Temporal Logics Using Large Language Models
Yongchao Chen; Rujul Gandhi; Yang Zhang; Chuchu Fan;
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Highlight: In this paper, we propose an accurate and generalizable transformation framework of English instructions from NL to TL, exploring the use of Large Language Models (LLMs) at multiple stages.
232, A Simple Baseline for Knowledge-Based Visual Question Answering
Alexandros Xenos; Themos Stafylakis; Ioannis Patras; Georgios Tzimiropoulos;
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Highlight: Our main contribution in this paper is to propose a much simpler and readily reproducible pipeline which, in a nutshell, is based on efficient in-context learning by prompting LLaMA (1 and 2) using question-informative captions as contextual information.
233, SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluation
Elizabeth Clark; Shruti Rijhwani; Sebastian Gehrmann; Joshua Maynez; Roee Aharoni; Vitaly Nikolaev; Thibault Sellam; Aditya Siddhant; Dipanjan Das; Ankur Parikh;
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Highlight: In this work, we introduce SEAHORSE, a dataset for multilingual, multifaceted summarization evaluation.
234, Deciphering Stereotypes in Pre-Trained Language Models
Weicheng Ma; Henry Scheible; Brian Wang; Goutham Veeramachaneni; Pratim Chowdhary; Alan Sun; Andrew Koulogeorge; Lili Wang; Diyi Yang; Soroush Vosoughi;
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Highlight: This paper addresses the issue of demographic stereotypes present in Transformer-based pre-trained language models (PLMs) and aims to deepen our understanding of how these biases are encoded in these models.
235, Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?
Yang Chen; Hexiang Hu; Yi Luan; Haitian Sun; Soravit Changpinyo; Alan Ritter; Ming-Wei Chang;
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Highlight: In this study, we introduce InfoSeek, a visual question answering dataset tailored for information-seeking questions that cannot be answered with only common sense knowledge.
236, Length Does Matter: Summary Length Can Bias Summarization Metrics
Xiaobo Guo; Soroush Vosoughi;
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Highlight: The results indicate that most metrics tend to favor longer summaries, even after accounting for other factors. To address this issue, we introduce a Bayesian normalization technique that effectively diminishes this bias.
237, Exploring Distributional Shifts in Large Language Models for Code Analysis
Shushan Arakelyan; Rocktim Das; Yi Mao; Xiang Ren;
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Highlight: We systematically study how three large language models with code capabilities - CodeT5, Codex, and ChatGPT - generalize to out-of-domain data.
238, Towards Building More Robust NER Datasets: An Empirical Study on NER Dataset Bias from A Dataset Difficulty View
Ruotian Ma; Xiaolei Wang; Xin Zhou; Qi Zhang; Xuanjing Huang;
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Highlight: Previous research attributes the robustness problem to the existence of NER dataset bias, where simpler and regular entity patterns induce shortcut learning. In this work, we bring new insights into this problem by comprehensively investigating the NER dataset bias from a dataset difficulty view.
239, Hallucination Detection for Generative Large Language Models By Bayesian Sequential Estimation
Xiaohua Wang; Yuliang Yan; Longtao Huang; Xiaoqing Zheng; Xuanjing Huang;
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Highlight: We introduce a unique framework that leverages statistical decision theory and Bayesian sequential analysis to optimize the trade-off between costs and benefits during the hallucination detection process.
240, Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4
Kellin Pelrine; Anne Imouza; Camille Thibault; Meilina Reksoprodjo; Caleb Gupta; Joel Christoph; Jean-Fran?ois Godbout; Reihaneh Rabbany;
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Highlight: We propose focusing on generalization, uncertainty, and how to leverage recent large language models, in order to create more practical tools to evaluate information veracity in contexts where perfect classification is impossible.
241, Exploring Discourse Structure in Document-level Machine Translation
Xinyu Hu; Xiaojun Wan;
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Highlight: In this paper, we present a more sound paragraph-to-paragraph translation mode and explore whether discourse structure can improve DocMT.
242, Evaluation Metrics in The Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks
Andrea Sottana; Bin Liang; Kai Zou; Zheng Yuan;
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Highlight: We aim to improve the understanding of current models? performance by providing a preliminary and hybrid evaluation on a range of open and closed-source generative LLMs on three NLP benchmarks: text summarisation, text simplification and grammatical error correction (GEC), using both automatic and human evaluation.
243, How Does Generative Retrieval Scale to Millions of Passages?
Ronak Pradeep; Kai Hui; Jai Gupta; Adam Lelkes; Honglei Zhuang; Jimmy Lin; Donald Metzler; Vinh Tran;
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Highlight: We conduct the first empirical study of generative retrieval techniques across various corpus scales, ultimately scaling up to the entire MS MARCO passage ranking task with a corpus of 8.
244, EtiCor: Corpus for Analyzing LLMs for Etiquettes
Ashutosh Dwivedi; Pradhyumna Lavania; Ashutosh Modi;
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Highlight: In this paper, we propose EtiCor, an Etiquettes Corpus, having texts about social norms from five different regions across the globe.
245, ViStruct: Visual Structural Knowledge Extraction Via Curriculum Guided Code-Vision Representation
Yangyi Chen; Xingyao Wang; Manling Li; Derek Hoiem; Heng Ji;
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Highlight: In this work, we present ViStruct, a training framework to learn VLMs for effective visual structural knowledge extraction.
246, Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at Scale
Marta Costa-juss?; Pierre Andrews; Eric Smith; Prangthip Hansanti; Christophe Ropers; Elahe Kalbassi; Cynthia Gao; Daniel Licht; Carleigh Wood;
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Highlight: We introduce a multilingual extension of the HolisticBias dataset, the largest English template-based taxonomy of textual people references: Multilingual HolisticBias.
247, NORMSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly
Yi Fung; Tuhin Chakrabarty; Hao Guo; Owen Rambow; Smaranda Muresan; Heng Ji;
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Highlight: Most computational research on norms has focused on a single culture, and manually built datasets, from non-conversational settings. We address these limitations by proposing a new framework, NormSage, to automatically extract culture-specific norms from multi-lingual conversations.
248, JASMINE: Arabic GPT Models for Few-Shot Learning
El Moatez Billah Nagoudi; Muhammad Abdul-Mageed; AbdelRahim Elmadany; Alcides Inciarte; Md Tawkat Islam Khondaker;
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Highlight: Using our novel benchmark, we evaluate JASMINE extensively showing powerful performance intrinsically as well as in few-shot learning on a wide range of NLP tasks. We aim to responsibly release our models and evaluation benchmark with interested researchers, along with code for experimenting with them.
249, Set Learning for Generative Information Extraction
Jiangnan Li; Yice Zhang; Bin Liang; Kam-Fai Wong; Ruifeng Xu;
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Highlight: Consequently, this formalization introduces a potential order bias, which can impair model learning. Targeting this issue, this paper proposes a set learning approach that considers multiple permutations of structured objects to optimize set probability approximately.
250, What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability
Mario Giulianelli; Joris Baan; Wilker Aziz; Raquel Fern?ndez; Barbara Plank;
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Highlight: For each test input, we measure the generator?s calibration to human production variability. Following this instance-level approach, we analyse NLG models and decoding strategies, demonstrating that probing a generator with multiple samples and, when possible, multiple references, provides the level of detail necessary to gain understanding of a model?s representation of uncertainty.
251, Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks
Xianzhi Li; Samuel Chan; Xiaodan Zhu; Yulong Pei; Zhiqiang Ma; Xiaomo Liu; Sameena Shah;
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Highlight: In this paper, we conduct empirical studies and provide experimental evidences of their performance on a wide variety of financial text analytical problems, using eight benchmark datasets from five categories of tasks.
252, ART: Rule BAsed FutuRe-inference DeducTion
Mengze Li; Tianqi Zhao; Bai Jionghao; Baoyi He; Jiaxu Miao; Wei Ji; Zheqi Lv; Zhou Zhao; Shengyu Zhang; Wenqiao Zhang; Fei Wu;
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Highlight: In this work, we introduce rule bAsed futuRe-inference deducTion (ART), which aims at deducing the correct future event based on the visual phenomenon (a video) and the rule-based premises, along with an explanation of the reasoning process.
253, Learning to Describe for Predicting Zero-shot Drug-Drug Interactions
Fangqi Zhu; Yongqi Zhang; Lei Chen; Bing Qin; Ruifeng Xu;
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Highlight: In this paper, we introduce a new problem setup as zero-shot DDI prediction that deals with the case of new drugs.
254, Learning Preference Model for LLMs Via Automatic Preference Data Generation
Shijia Huang; Jianqiao Zhao; Yanyang Li; Liwei Wang;
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Highlight: In this paper, we propose learning the preference model for LLMs via automatic preference data generation (AutoPM).
255, Bridging The Gap Between Synthetic and Authentic Images for Multimodal Machine Translation
Wenyu Guo; Qingkai Fang; Dong Yu; Yang Feng;
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Highlight: Consequently, using authentic images for training and synthetic images for inference can introduce a distribution shift, resulting in performance degradation during inference. To tackle this challenge, in this paper, we feed synthetic and authentic images to the MMT model, respectively.
256, Pushdown Layers: Encoding Recursive Structure in Transformer Language Models
Shikhar Murty; Pratyusha Sharma; Jacob Andreas; Christopher Manning;
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Highlight: This work introduces Pushdown Layers, a new self-attention layer that models recursive state via a stack tape that tracks estimated depths of every token in an incremental parse of the observed prefix.
257, BiasX: ?Thinking Slow? in Toxic Content Moderation with Explanations of Implied Social Biases
Yiming Zhang; Sravani Nanduri; Liwei Jiang; Tongshuang Wu; Maarten Sap;
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Highlight: This can lead to subtle toxicity being missed, and seemingly toxic but harmless content being over-detected. We introduce BiasX, a framework that enhances content moderation setups with free-text explanations of statements? implied social biases, and explore its effectiveness through a large-scale crowdsourced user study.
258, Don?t Take This Out of Context!: On The Need for Contextual Models and Evaluations for Stylistic Rewriting
Akhila Yerukola; Xuhui Zhou; Elizabeth Clark; Maarten Sap;
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Highlight: In this paper, we investigate integrating the preceding textual context into both the rewriting and evaluation stages of stylistic text rewriting, and introduce a new composite contextual evaluation metric CtxSimFit that combines similarity to the original sentence with contextual cohesiveness.
259, FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions
Hyunwoo Kim; Melanie Sclar; Xuhui Zhou; Ronan Bras; Gunhee Kim; Yejin Choi; Maarten Sap;
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Highlight: We introduce FANToM, a new benchmark designed to stress-test ToM within information-asymmetric conversational contexts via question answering.
260, LLM-FP4: 4-Bit Floating-Point Quantized Transformers
Shih-yang Liu; Zechun Liu; Xijie Huang; Pingcheng Dong; Kwang-Ting Cheng;
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Highlight: We propose LLM-FP4 for quantizing both weights and activations in large language models (LLMs) down to 4-bit floating-point values, in a post-training manner.
261, Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
Yizhu Jiao; Ming Zhong; Sha Li; Ruining Zhao; Siru Ouyang; Heng Ji; Jiawei Han;
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Highlight: However, when it comes to information extraction ? a classic task in natural language processing ? most task-specific systems cannot align well with long-tail ad hoc extraction use cases for non-expert users. To address this, we propose a novel paradigm, termed On-Demand Information Extraction, to fulfill the personalized demands of real-world users.
262, AutoTrial: Prompting Language Models for Clinical Trial Design
Zifeng Wang; Cao Xiao; Jimeng Sun;
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Highlight: In this paper, we present a method named AutoTrial to aid the design of clinical eligibility criteria using language models.
263, A Unified View of Evaluation Metrics for Structured Prediction
Yunmo Chen; William Gantt; Tongfei Chen; Aaron White; Benjamin Van Durme;
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Highlight: We present a conceptual framework that unifies a variety of evaluation metrics for different structured prediction tasks (e. g. event and relation extraction, syntactic and semantic parsing).
264, WordArt Designer: User-Driven Artistic Typography Synthesis Using Large Language Models
Jun-Yan He; Zhi-Qi Cheng; Chenyang Li; Jingdong Sun; Wangmeng Xiang; Xianhui Lin; Xiaoyang Kang; Zengke Jin; Yusen Hu; Bin Luo; Yifeng Geng; Xuansong Xie;
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Highlight: This paper introduces WordArt Designer, a user-driven framework for artistic typography synthesis, relying on the Large Language Model (LLM).
265, QTSumm: Query-Focused Summarization Over Tabular Data
Yilun Zhao; Zhenting Qi; Linyong Nan; Boyu Mi; Yixin Liu; Weijin Zou; Simeng Han; Ruizhe Chen; Xiangru Tang; Yumo Xu; Dragomir Radev; Arman Cohan;
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Highlight: Motivated by this, we define a new query-focused table summarization task, where text generation models have to perform human-like reasoning and analysis over the given table to generate a tailored summary. We introduce a new benchmark named QTSumm for this task, which contains 7,111 human-annotated query-summary pairs over 2,934 tables covering diverse topics.
266, Investigating Table-to-Text Generation Capabilities of Large Language Models in Real-World Information Seeking Scenarios
Yilun Zhao; Haowei Zhang; Shengyun Si; Linyong Nan; Xiangru Tang; Arman Cohan;
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Highlight: In this paper, we investigate the table-to-text capabilities of different LLMs using four datasets within two real-world information seeking scenarios.
267, CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks
Mete Ismayilzada; Debjit Paul; Syrielle Montariol; Mor Geva; Antoine Bosselut;
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Highlight: In this work, we present CRoW, a manually-curated, multi-task benchmark that evaluates the ability of models to apply commonsense reasoning in the context of six real-world NLP tasks.
268, CRAB: Assessing The Strength of Causal Relationships Between Real-world Events
Angelika Romanou; Syrielle Montariol; Debjit Paul; Leo Laugier; Karl Aberer; Antoine Bosselut;
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Highlight: In this work, we present CRAB, a new Causal Reasoning Assessment Benchmark designed to evaluate causal understanding of events in real-world narratives.
269, SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables
Xinyuan Lu; Liangming Pan; Qian Liu; Preslav Nakov; Min-Yen Kan;
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Highlight: We present SCITAB, a challenging evaluation dataset consisting of 1.
270, CaseEncoder: A Knowledge-enhanced Pre-trained Model for Legal Case Encoding
Yixiao Ma; Yueyue Wu; Weihang Su; Qingyao Ai; Yiqun Liu;
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Highlight: Specifically, these models may not fully capture the underlying legal features in legal case documents. To address this issue, we propose CaseEncoder, a legal document encoder that leverages fine-grained legal knowledge in both the data sampling and pre-training phases.
271, LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction Following
Cheng-Fu Yang; Yen-Chun Chen; Jianwei Yang; Xiyang Dai; Lu Yuan; Yu-Chiang Wang; Kai-Wei Chang;
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Highlight: This lack of generalizability is due to the agent?s insensitivity to subtle changes in natural language instructions. To mitigate this issue, we propose explicitly aligning the agent?s hidden states with the instructions via contrastive learning.
272, Counter Turing Test (CT2): AI-Generated Text Detection Is Not As Easy As You May Think - Introducing AI Detectability Index (ADI)
Megha Chakraborty; S.M Towhidul Islam Tonmoy; S M Mehedi Zaman; Shreya Gautam; Tanay Kumar; Krish Sharma; Niyar Barman; Chandan Gupta; Vinija Jain; Aman Chadha; Amit Sheth; Amitava Das;
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Highlight: Thus, to establish a quantifiable spectrum facilitating the evaluation and ranking of LLMs according to their detectability levels, we propose the AI Detectability Index (ADI).
273, FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability Through 5W Question-Answering
Megha Chakraborty; Khushbu Pahwa; Anku Rani; Shreyas Chatterjee; Dwip Dalal; Harshit Dave; Ritvik G; Preethi Gurumurthy; Adarsh Mahor; Samahriti Mukherjee; Aditya Pakala; Ishan Paul; Janvita Reddy; Arghya Sarkar; Kinjal Sensharma; Aman Chadha; Amit Sheth; Amitava Das;
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Highlight: Despite progress in automatic text-based fact verification (e. g. , FEVER, LIAR), the research community lacks substantial effort in multimodal fact verification. To address this gap, we introduce FACTIFY 3M, a dataset of 3 million samples that pushes the boundaries of the domain of fact verification via a multimodal fake news dataset, in addition to offering explainability through the concept of 5W question-answering.
274, Simple and Effective Input Reformulations for Translation
Brian Yu; Hansen Lillemark; Kurt Keutzer;
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Highlight: In this paper, we reformulate inputs during finetuning for challenging translation tasks, leveraging model strengths from pretraining in novel ways to improve downstream performance.
275, The Sentiment Problem: A Critical Survey Towards Deconstructing Sentiment Analysis
Pranav Venkit; Mukund Srinath; Sanjana Gautam; Saranya Venkatraman; Vipul Gupta; Rebecca Passonneau; Shomir Wilson;
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Highlight: Our study exposes a lack of explicit definitions and frameworks for characterizing sentiment, resulting in potential challenges and biases. To tackle this issue, we propose an ethics sheet encompassing critical inquiries to guide practitioners in ensuring equitable utilization of SA.
276, PAC-tuning: Fine-tuning Pre-trained Language Models with PAC-driven Perturbed Gradient Descent
Guangliang Liu; Zhiyu Xue; Xitong Zhang; Kristen Johnson; Rongrong Wang;
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Highlight: However, adding these regularizations necessitates heavy tuning of the hyperparameters of optimization algorithms, such as the popular Adam optimizer. In this paper, we propose a two-stage fine-tuning method, PAC-tuning, to address this optimization challenge.
277, Unveiling The Implicit Toxicity in Large Language Models
Jiaxin Wen; Pei Ke; Hao Sun; Zhexin Zhang; Chengfei Li; Jinfeng Bai; Minlie Huang;
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Highlight: While recent studies primarily focus on probing toxic outputs that can be easily detected with existing toxicity classifiers, we show that LLMs can generate diverse implicit toxic outputs that are exceptionally difficult to detect via simply zero-shot prompting.
278, Re3Dial: Retrieve, Reorganize and Rescale Conversations for Long-Turn Open-Domain Dialogue Pre-training
Jiaxin Wen; Hao Zhou; Jian Guan; Jie Zhou; Minlie Huang;
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Highlight: Most dialogues in existing pre-training corpora contain fewer than three turns of dialogue. To alleviate this issue, we propose the Retrieve, Reorganize and Rescale framework (Re3Dial), which can automatically construct billion-scale long-turn dialogues by reorganizing existing short-turn ones.
279, Multi-Source Probing for Open-Domain Conversational Understanding
Yuanxi Li; Hao Zhou; Jie Zhou; Minlie Huang;
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Highlight: In this study, we propose a Multi-Source Probing (MSP) method to probe the dialogue comprehension abilities of open-domain dialogue models.
280, Building Multi-domain Dialog State Trackers from Single-domain Dialogs
Qi Zhu; Zheng Zhang; Xiaoyan Zhu; Minlie Huang;
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Highlight: In this paper, we propose a divide-and-conquer (DAC) DST paradigm and a multi-domain dialog synthesis framework, which makes building multi-domain DST models from single-domain dialogs possible.
281, SPT: Learning to Selectively Insert Prompts for Better Prompt Tuning
Wei Zhu; Ming Tan;
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Highlight: In this work, we propose a novel framework, Selective Prompt Tuning (SPT), that learns to select the proper prompt layers by inserting a prompt controlled by a learnable probabilistic gate at each intermediate layer.
282, Enhancing Structured Evidence Extraction for Fact Verification
Zirui Wu; Nan Hu; Yansong Feng;
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Highlight: In this paper, we propose a simple but effective method to enhance the extraction of structured evidence by leveraging the row and column semantics of tables.
283, UniMath: A Foundational and Multimodal Mathematical Reasoner
Zhenwen Liang; Tianyu Yang; Jipeng Zhang; Xiangliang Zhang;
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Highlight: While significant progress has been made in natural language processing (NLP), existing methods exhibit limitations in effectively interpreting and processing diverse mathematical modalities. Therefore, we introduce UniMath, a versatile and unified system designed for multimodal mathematical reasoning tasks.
284, SLOG: A Structural Generalization Benchmark for Semantic Parsing
Bingzhi Li; Lucia Donatelli; Alexander Koller; Tal Linzen; Yuekun Yao; Najoung Kim;
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Highlight: We introduce SLOG, a semantic parsing dataset that extends COGS (Kim and Linzen, 2020) with 17 structural generalization cases.
285, Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering
Wang Zhu; Jesse Thomason; Robin Jia;
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Highlight: We propose Chain-of-Questions, a framework that trains a model to robustly answer multistep questions by generating and answering sub-questions.
286, Memory-Based Invariance Learning for Out-of-Domain Text Classification
Chen Jia; Yue Zhang;
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Highlight: Specifically, we augment the original feature space using key-value memory and employ a meta-learning-based approach to enhance the quality of the invariant representations.
287, Hi-ArG: Exploring The Integration of Hierarchical Argumentation Graphs in Language Pretraining
Jingcong Liang; Rong Ye; Meng Han; Qi Zhang; Ruofei Lai; Xinyu Zhang; Zhao Cao; Xuanjing Huang; Zhongyu Wei;
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Highlight: In this paper, we propose the **Hi**erarchical **Ar**gumentation **G**raph (Hi-ArG), a new structure to organize arguments.
288, Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation
Jiayu Lin; Rong Ye; Meng Han; Qi Zhang; Ruofei Lai; Xinyu Zhang; Zhao Cao; Xuanjing Huang; Zhongyu Wei;
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Highlight: In this paper, we present the ArgTersely benchmark for sentence-level counter-argument generation, drawing from a manually annotated dataset from the ChangeMyView debate forum.
289, When Do Decompositions Help for Machine Reading?
Kangda Wei; Dawn Lawrie; Benjamin Van Durme; Yunmo Chen; Orion Weller;
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Highlight: We find that decompositions can be helpful in zero or limited-data settings, giving several points of improvement in exact match.
290, Towards Conceptualization of ?Fair Explanation?: Disparate Impacts of Anti-Asian Hate Speech Explanations on Content Moderators
Tin Nguyen; Jiannan Xu; Aayushi Roy; Hal Daum? III; Marine Carpuat;
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Highlight: We propose to characterize what constitutes an explanation that is itself ?fair? ? an explanation that does not adversely impact specific populations.
291, Expand, Highlight, Generate: RL-driven Document Generation for Passage Reranking
Arian Askari; Mohammad Aliannejadi; Chuan Meng; Evangelos Kanoulas; Suzan Verberne;
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Highlight: In this paper, we propose a new perspective of data augmentation: generating synthetic documents from queries.
292, E-THERAPIST: I Suggest You to Cultivate A Mindset of Positivity and Nurture Uplifting Thoughts
Kshitij Mishra; Priyanshu Priya; Manisha Burja; Asif Ekbal;
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Highlight: Focusing on this objective, we propose e-THERAPIST, a novel polite interpersonal psychotherapy dialogue system to address issues like depression, anxiety, schizophrenia, etc.
293, PHD: Pixel-Based Language Modeling of Historical Documents
Nadav Borenstein; Phillip Rust; Desmond Elliott; Isabelle Augenstein;
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Highlight: Due to the scarcity of real historical scans, we propose a novel method for generating synthetic scans to resemble real historical documents.
294, Towards LLM-driven Dialogue State Tracking
Yujie Feng; Zexin Lu; Bo Liu; Liming Zhan; Xiao-Ming Wu;
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Highlight: In this study, we conduct an initial examination of ChatGPT?s capabilities in DST.
295, LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models
Huiqiang Jiang; Qianhui Wu; Chin-Yew Lin; Yuqing Yang; Lili Qiu;
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Highlight: To accelerate model inference and reduce cost, this paper presents LLMLingua, a coarse-to-fine prompt compression method that involves a budget controller to maintain semantic integrity under high compression ratios, a token-level iterative compression algorithm to better model the interdependence between compressed contents, and an instruction tuning based method for distribution alignment between language models.
296, Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration
Yiquan Wu; Siying Zhou; Yifei Liu; Weiming Lu; Xiaozhong Liu; Yating Zhang; Changlong Sun; Fei Wu; Kun Kuang;
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Highlight: In this paper, we propose the precedent-enhanced LJP framework (PLJP) ? a system that leverages the strength of both LLM and domain models in the context of precedents.
297, The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment
Jared Fernandez; Jacob Kahn; Clara Na; Yonatan Bisk; Emma Strubell;
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Highlight: We denote this phenomena as the framework tax, and observe that the disparity is growing as hardware speed increases over time. In this work, we examine this phenomena through a series of case studies analyzing the effects of model design decisions, framework paradigms, and hardware platforms on total model latency.
298, Once Is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling
Yuanhang Yang; Shiyi Qi; Chuanyi Liu; Qifan Wang; Cuiyun Gao; Zenglin Xu;
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Highlight: To this end, this paper introduces a novel paradigm TopicAns for efficient sentence pair modeling.
299, Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation
Bashar Alhafni; Go Inoue; Christian Khairallah; Nizar Habash;
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Highlight: In this paper, we present the first results on Arabic GEC using two newly developed Transformer-based pretrained sequence-to-sequence models.
300, On Bilingual Lexicon Induction with Large Language Models
Yaoyiran Li; Anna Korhonen; Ivan Vulic;
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Highlight: Inspired by the global paradigm shift in NLP towards Large Language Models (LLMs), we examine the potential of the latest generation of LLMs for the development of bilingual lexicons.
301, Multi-teacher Distillation for Multilingual Spelling Correction
Jingfen Zhang; Xuan Guo; Sravan Bodapati; Christopher Potts;
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Highlight: For services that are deployed around the world, this poses a significant challenge for multilingual NLP: spelling errors need to be caught and corrected in all languages, and even in queries that use multiple languages. In this paper, we tackle this challenge using multi-teacher distillation.
302, Simple Temporal Adaptation to Changing Label Sets: Hashtag Prediction Via Dense KNN
Niloofar Mireshghallah; Nikolai Vogler; Junxian He; Omar Florez; Ahmed El-Kishky; Taylor Berg-Kirkpatrick;
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Highlight: In this paper, we study temporal adaptation through the task of longitudinal hashtag prediction and propose a non-parametric dense retrieval technique, which does not require re-training, as a simple but effective solution.
303, STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection Via News Label Diffusion
Xurui Li; Yue Qin; Rui Zhu; Tianqianjin Lin; Yongming Fan; Yangyang Kang; Kaisong Song; Fubang Zhao; Changlong Sun; Haixu Tang; Xiaozhong Liu;
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Highlight: However, unaffordable large-scale annotation as well as training data sparseness barrier the full exploitation of commercial news in risk detection. To address this problem, we propose a semi-supervised Semantic-Topological Iteration Network, STINMatch, along with a news-enterprise knowledge graph (NEKG) to endorse the risk detection enhancement.
304, CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning Without Full Large Language Model
Kaiyan Zhang; Ning Ding; Biqing Qi; Xuekai Zhu; Xinwei Long; Bowen Zhou;
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Highlight: Simultaneously, we note subtle but potentially significant changes in representation and intermediate predictions across the layers. Inspired by these observations, we propose CRaSh, involving Clustering, Removing, and Sharing, a training-free strategy to derive improved emulators from LLMs.
305, CS2W: A Chinese Spoken-to-Written Style Conversion Dataset with Multiple Conversion Types
Zishan Guo; Linhao Yu; Minghui Xu; Renren Jin; Deyi Xiong;
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Highlight: Unfortunately, the availability of datasets for this is limited. To address this issue, we present CS2W, a Chinese Spoken-to-Written style conversion dataset comprising 7,237 spoken sentences extracted from transcribed conversational texts.
306, Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality
Harman Singh; Pengchuan Zhang; Qifan Wang; Mengjiao Wang; Wenhan Xiong; Jingfei Du; Yu Chen;
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Highlight: In this work, we consider the scene graph parsed from text as a proxy for the image scene graph and propose a graph decomposition and augmentation framework along with a coarse-to-fine contrastive learning objective between images and text that aligns sentences of various complexities to the same image.
307, Discourse Structures Guided Fine-grained Propaganda Identification
Yuanyuan Lei; Ruihong Huang;
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Highlight: In this paper, we aim to identify propaganda in political news at two fine-grained levels: sentence-level and token-level.
308, Fidelity-Enriched Contrastive Search: Reconciling The Faithfulness-Diversity Trade-Off in Text Generation
Wei-Lin Chen; Cheng-Kuang Wu; Hsin-Hsi Chen; Chung-Chi Chen;
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Highlight: In this paper, we address the hallucination problem commonly found in natural language generation tasks.
309, Location-Aware Visual Question Generation with Lightweight Models
Nicholas Suwono; Justin Chen; Tun Hung; Ting-Hao Huang; I-Bin Liao; Yung-Hui Li; Lun-Wei Ku; Shao-Hua Sun;
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Highlight: Specifically, we represent such location-aware information with surrounding images and a GPS coordinate. To tackle this task, we present a dataset generation pipeline that leverages GPT-4 to produce diverse and sophisticated questions.
310, GPT-RE: In-context Learning for Relation Extraction Using Large Language Models
Zhen Wan; Fei Cheng; Zhuoyuan Mao; Qianying Liu; Haiyue Song; Jiwei Li; Sadao Kurohashi;
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Highlight: In this paper, we propose GPT-RE to successfully address the aforementioned issues by (1) incorporating task-aware representations in demonstration retrieval; and (2) enriching the demonstrations with gold label-induced reasoning logic.
311, Sociocultural Norm Similarities and Differences Via Situational Alignment and Explainable Textual Entailment
Sky CH-Wang; Arkadiy Saakyan; Oliver Li; Zhou Yu; Smaranda Muresan;
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Highlight: Here, we propose a novel approach to discover and compare descriptive social norms across Chinese and American cultures.
312, RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction
Shiao Meng; Xuming Hu; Aiwei Liu; Shuang Li; Fukun Ma; Yawen Yang; Lijie Wen;
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Highlight: In this paper, we propose a relation-aware prototype learning method for FSDLRE to strengthen the relational semantics of prototype representations.
313, When The Majority Is Wrong: Modeling Annotator Disagreement for Subjective Tasks
Eve Fleisig; Rediet Abebe; Dan Klein;
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Highlight: Thus, a crucial problem in hate speech detection is determining if a statement is offensive to the demographic group that it targets, when that group may be a small fraction of the annotator pool. We construct a model that predicts individual annotator ratings on potentially offensive text and combines this information with the predicted target group of the text to predict the ratings of target group members.
314, Characterizing Mechanisms for Factual Recall in Language Models
Qinan Yu; Jack Merullo; Ellie Pavlick;
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Highlight: On a dataset that queries for knowledge of world capitals, we investigate both distributional and mechanistic determinants of LM behavior in such situations.
315, DNA: Denoised Neighborhood Aggregation for Fine-grained Category Discovery
Wenbin An; Feng Tian; Wenkai Shi; Yan Chen; Qinghua Zheng; QianYing Wang; Ping Chen;
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Highlight: In this paper, we propose Denoised Neighborhood Aggregation (DNA), a self-supervised framework that encodes semantic structures of data into the embedding space.
316, Context Compression for Auto-regressive Transformers with Sentinel Tokens
Siyu Ren; Qi Jia; Kenny Zhu;
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Highlight: In this work, we propose a plug-and-play approach that is able to incrementally compress the intermediate activation of a specified span of tokens into compact ones, thereby reducing both memory and computational cost when processing subsequent context.
317, Quantifying Character Similarity with Vision Transformers
Xinmei Yang; Abhishek Arora; Shao-Yu Jheng; Melissa Dell;
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Highlight: This study develops an extensible way to measure character substitution costs for OCR?ed documents, by employing large-scale self-supervised training of vision transformers (ViT) with augmented digital fonts.
318, BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification
Mithun Das; Animesh Mukherjee;
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Highlight: The problem becomes more challenging in a low-resource setting (e. g. , Bengali memes, i. e. , images with Bengali text embedded on it) because of the absence of benchmark datasets on which AI models could be trained. In this paper we bridge this gap by building a Bengali meme dataset.
319, TrojanSQL: SQL Injection Against Natural Language Interface to Database
Jinchuan Zhang; Yan Zhou; Binyuan Hui; Yaxin Liu; Ziming Li; Songlin Hu;
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Highlight: By proposing TrojanSQL, a backdoor-based SQL injection framework for text-to-SQL systems, we show how state-of-the-art text-to-SQL parsers can be easily misled to produce harmful SQL statements that can invalidate user queries or compromise sensitive information about the database.
320, How Do Languages Influence Each Other? Studying Cross-lingual Data Sharing During LM Fine-tuning
Rochelle Choenni; Dan Garrette; Ekaterina Shutova;
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Highlight: Yet, it remains unclear to what extent, and under which conditions, languages rely on each other?s data. To answer this question, we use TracIn (Pruthi et al. , 2020), a training data attribution (TDA) method, to retrieve training samples from multilingual data that are most influential for test predictions in a given language.
321, Exploring Chain of Thought Style Prompting for Text-to-SQL
Chang-Yu Tai; Ziru Chen; Tianshu Zhang; Xiang Deng; Huan Sun;
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Highlight: In this paper, we hypothesize that a crucial aspect of LLMs to improve for text-to-SQL parsing is their multi-step reasoning ability.
322, ClimateBERT-NetZero: Detecting and Assessing Net Zero and Reduction Targets
Tobias Schimanski; Julia Bingler; Mathias Kraus; Camilla Hyslop; Markus Leippold;
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Highlight: Public and private actors struggle to assess the vast amounts of information about sustainability commitments made by various institutions. To address this problem, we create a novel tool for automatically detecting corporate and national net zero and reduction targets in three steps.
323, Primacy Effect of ChatGPT
Yiwei Wang; Yujun Cai; Muhao Chen; Yuxuan Liang; Bryan Hooi;
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Highlight: In this paper, we study the primacy effect of ChatGPT: the tendency of selecting the labels at earlier positions as the answer.
324, Rethinking and Improving Multi-task Learning for End-to-end Speech Translation
Yuhao Zhang; Chen Xu; Bei Li; Hao Chen; Tong Xiao; Chunliang Zhang; Jingbo Zhu;
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Highlight: In this paper, we investigate the consistency between different tasks, considering different times and modules.
325, MailEx: Email Event and Argument Extraction
Saurabh Srivastava; Gaurav Singh; Shou Matsumoto; Ali Raz; Paulo Costa; Joshua Poore; Ziyu Yao;
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Highlight: In this work, we present the first dataset, MailEx, for performing event extraction from conversational email threads.
326, Multilingual Large Language Models Are Not (Yet) Code-Switchers
Ruochen Zhang; Samuel Cahyawijaya; Jan Christian Blaise Cruz; Genta Winata; Alham Aji;
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Highlight: In this paper, we provide a comprehensive empirical analysis of various multilingual LLMs, benchmarking their performance across four tasks: sentiment analysis, machine translation, summarization and word-level language identification.
327, ALCUNA: Large Language Models Meet New Knowledge
Xunjian Yin; Baizhou Huang; Xiaojun Wan;
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Highlight: In this paper, we address the lack of benchmarks to evaluate LLMs? ability to handle new knowledge, an important and challenging aspect in the rapidly evolving world.
328, Models See Hallucinations: Evaluating The Factuality in Video Captioning
Hui Liu; Xiaojun Wan;
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Highlight: In this work, we conduct the first human evaluation of the factuality in video captioning and annotate two factuality datasets.
329, Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations
Wei-Lin Chen; Cheng-Kuang Wu; Yun-Nung Chen; Hsin-Hsi Chen;
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Highlight: In this work, we introduce Self-ICL?a simple framework which bootstraps LMs? intrinsic capabilities to perform zero-shot ICL.
330, CRT-QA: A Dataset of Complex Reasoning Question Answering Over Tabular Data
Zhehao Zhang; Xitao Li; Yan Gao; Jian-Guang Lou;
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Highlight: In this work, we first establish a comprehensive taxonomy of reasoning and operation types for tabular data analysis. Then, we construct a complex reasoning QA dataset over tabular data, named CRT-QA dataset (Complex Reasoning QA over Tabular data), with the following unique features: (1) it is the first Table QA dataset with multi-step operation and informal reasoning; (2) it contains fine-grained annotations on questions? directness, composition types of sub-questions, and human reasoning paths which can be used to conduct a thorough investigation on LLMs? reasoning ability; (3) it contains a collection of unanswerable and indeterminate questions that commonly arise in real-world situations.
331, Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding
Sangmin Bae; Jongwoo Ko; Hwanjun Song; Se-Young Yun;
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Highlight: Consequently, we propose a Fast and Robust Early-Exiting (FREE) framework, which incorporates a shallow-deep module and a synchronized parallel decoding.
332, The Benefits of Label-Description Training for Zero-Shot Text Classification
Lingyu Gao; Debanjan Ghosh; Kevin Gimpel;
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Highlight: We propose a simple way to further improve zero-shot accuracies with minimal effort.
333, Crystal: Introspective Reasoners Reinforced with Self-Feedback
Jiacheng Liu; Ramakanth Pasunuru; Hannaneh Hajishirzi; Yejin Choi; Asli Celikyilmaz;
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Highlight: We propose a novel method to develop an introspective commonsense reasoner, **Crystal**.
334, Reducing Sequence Length By Predicting Edit Spans with Large Language Models
Masahiro Kaneko; Naoaki Okazaki;
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Highlight: This paper proposes predicting edit spans for the source text for local sequence transduction tasks.
335, CompoundPiece: Evaluating and Improving Decompounding Performance of Language Models
Benjamin Minixhofer; Jonas Pfeiffer; Ivan Vulic;
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Highlight: In this work, we systematically study decompounding, the task of splitting compound words into their constituents, at a wide scale.
336, Revisiting The Optimality of Word Lengths
Tiago Pimentel; Clara Meister; Ethan Wilcox; Kyle Mahowald; Ryan Cotterell;
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Highlight: In this work, we show that Piantadosi et al. ?s derivation does not minimize CCH?s cost, but rather a lower bound, which we term CCH-lower.
337, Evaluating and Modeling Attribution for Cross-Lingual Question Answering
Benjamin Muller; John Wieting; Jonathan Clark; Tom Kwiatkowski; Sebastian Ruder; Livio Soares; Roee Aharoni; Jonathan Herzig; Xinyi Wang;
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Highlight: We find that Natural Language Inference models and PaLM 2 fine-tuned on a very small amount of attribution data can accurately detect attribution. With these models, we improve the attribution level of a cross-lingual QA system.
338, Information Value: Measuring Utterance Predictability As Distance from Plausible Alternatives
Mario Giulianelli; Sarenne Wallbridge; Raquel Fern?ndez;
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Highlight: We introduce a method to obtain interpretable estimates of information value using neural text generators, and exploit their psychometric predictive power to investigate the dimensions of predictability that drive human comprehension behaviour.
339, Target-to-Source Augmentation for Aspect Sentiment Triplet Extraction
Yice Zhang; Yifan Yang; Meng Li; Bin Liang; Shiwei Chen; Ruifeng Xu;
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Highlight: However, applying these methods to fine-grained tasks like ASTE poses challenges in generating diverse augmented samples while maintaining alignment between modified sentences and origin labels. Therefore, this paper proposes a target-to-source augmentation approach for ASTE.
340, Stance Detection on Social Media with Background Knowledge
Ang Li; Bin Liang; Jingqian Zhao; Bowen Zhang; Min Yang; Ruifeng Xu;
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Highlight: In this paper, we investigate stance detection from a novel perspective, where the background knowledge of the targets is taken into account for better stance detection.
341, Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs
Simone Conia; Min Li; Daniel Lee; Umar Minhas; Ihab Ilyas; Yunyao Li;
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Highlight: However, when it comes to non-English languages, the quantity and quality of textual information are comparatively scarce. To address this issue, we introduce the novel task of automatic Knowledge Graph Completion (KGE) and perform a thorough investigation on bridging the gap in both the quantity and quality of textual information between English and non-English languages.
342, Is ChatGPT Good at Search? Investigating Large Language Models As Re-Ranking Agents
Weiwei Sun; Lingyong Yan; Xinyu Ma; Shuaiqiang Wang; Pengjie Ren; Zhumin Chen; Dawei Yin; Zhaochun Ren;
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Highlight: In this paper, we first investigate generative LLMs such as ChatGPT and GPT-4 for relevance ranking in IR. Surprisingly, our experiments reveal that properly instructed LLMs can deliver competitive, even superior results to state-of-the-art supervised methods on popular IR benchmarks.
343, Content- and Topology-Aware Representation Learning for Scientific Multi-Literature
Kai Zhang; Kaisong Song; Yangyang Kang; Xiaozhong Liu;
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Highlight: In this paper, we propose SMRC2, which extends representation learning to the multi-document level.
344, Countering Misinformation Via Emotional Response Generation
Daniel Russo; Shane Kaszefski-Yaschuk; Jacopo Staiano; Marco Guerini;
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Highlight: Thereby, significant effort has been made to automate the use of fact-checker material in social correction; however, no previous work has tried to integrate it with the style and pragmatics that are commonly employed in social media communication. To fill this gap, we present VerMouth, the first large-scale dataset comprising roughly 12 thousand claim-response pairs (linked to debunking articles), accounting for both SMP-style and basic emotions, two factors which have a significant role in misinformation credibility and spreading.
345, Dual-Feedback Knowledge Retrieval for Task-Oriented Dialogue Systems
Tianyuan Shi; Liangzhi Li; Zijian Lin; Tao Yang; Xiaojun Quan; Qifan Wang;
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Highlight: Taking inspiration from open-domain question answering, we propose a retriever-generator architecture that harnesses a retriever to retrieve pertinent knowledge and a generator to generate system responses.
346, Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning Across Languages
Libo Qin; Qiguang Chen; Fuxuan Wei; Shijue Huang; Wanxiang Che;
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Highlight: In this work, we introduce cross-lingual prompting (CLP), aiming to improve zero-shot CoT reasoning across languages.
347, Dancing Between Success and Failure: Edit-level Simplification Evaluation Using SALSA
David Heineman; Yao Dou; Mounica Maddela; Wei Xu;
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Highlight: Large language models (e. g. , GPT-4) are uniquely capable of producing highly rated text simplification, yet current human evaluation methods fail to provide a clear understanding of systems? specific strengths and weaknesses. To address this limitation, we introduce SALSA, an edit-based human annotation framework that enables holistic and fine-grained text simplification evaluation.
348, End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions
Libo Qin; Wenbo Pan; Qiguang Chen; Lizi Liao; Zhou Yu; Yue Zhang; Wanxiang Che; Min Li;
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Highlight: In this paper, we present a thorough review and provide a unified perspective to summarize existing approaches as well as recent trends to advance the development of EToD research.
349, Tree Prompting: Efficient Task Adaptation Without Fine-Tuning
Chandan Singh; John Morris; Alexander Rush; Jianfeng Gao; Yuntian Deng;
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Highlight: Tree Prompting is an approach to prompting which builds a decision tree of prompts, linking multiple prompt-LM calls together to solve a task.
350, Improving Diversity of Demographic Representation in Large Language Models Via Collective-Critiques and Self-Voting
Preethi Lahoti; Nicholas Blumm; Xiao Ma; Raghavendra Kotikalapudi; Sahitya Potluri; Qijun Tan; Hansa Srinivasan; Ben Packer; Ahmad Beirami; Alex Beutel; Jilin Chen;
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Highlight: In this paper, we formalize the problem diversity of representation in LLM generations.
351, Bridging Information-Theoretic and Geometric Compression in Language Models
Emily Cheng; Corentin Kervadec; Marco Baroni;
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Highlight: We propose analyzing compression in (pre-trained) LMs from two points of view: geometric and information-theoretic.
352, Interactive Text-to-SQL Generation Via Editable Step-by-Step Explanations
Yuan Tian; Zheng Zhang; Zheng Ning; Toby Li; Jonathan Kummerfeld; Tianyi Zhang;
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Highlight: Many techniques have been proposed to automatically generate SQL from natural language, but they suffer from two issues: (1) they still make many mistakes, particularly for complex queries, and (2) they do not provide a flexible way for non-expert users to validate and refine incorrect queries. To address these issues, we introduce a new interaction mechanism that allows users to directly edit a step-by-step explanation of a query to fix errors.
353, Better Quality Pre-training Data and T5 Models for African Languages
Akintunde Oladipo; Mofetoluwa Adeyemi; Orevaoghene Ahia; Abraham Owodunni; Odunayo Ogundepo; David Adelani; Jimmy Lin;
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Highlight: In this study, we highlight the importance of enhancing the quality of pretraining data in multilingual language models.
354, Prompt As Triggers for Backdoor Attack: Examining The Vulnerability in Language Models
Shuai Zhao; Jinming Wen; Anh Luu; Junbo Zhao; Jie Fu;
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Highlight: In this study, we propose ProAttack, a novel and efficient method for performing clean-label backdoor attacks based on the prompt, which uses the prompt itself as a trigger.
355, CorefPrompt: Prompt-based Event Coreference Resolution By Measuring Event Type and Argument Compatibilities
Sheng Xu; Peifeng Li; Qiaoming Zhu;
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Highlight: Most previous studies adopt the ?encoding first, then scoring? framework, making the coreference judgment rely on event encoding. Furthermore, current methods struggle to leverage human-summarized ECR rules, e. g. , coreferential events should have the same event type, to guide the model. To address these two issues, we propose a prompt-based approach, CorefPrompt, to transform ECR into a cloze-style MLM (masked language model) task.
356, FOCUS: Effective Embedding Initialization for Monolingual Specialization of Multilingual Models
Konstantin Dobler; Gerard de Melo;
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Highlight: In this paper, we propose FOCUS - **F**ast **O**verlapping Token **C**ombinations **U**sing **S**parsemax, a novel embedding initialization method that effectively initializes the embedding matrix for a new tokenizer based on information in the source model?s embedding matrix.
357, NeuSTIP: A Neuro-Symbolic Model for Link and Time Prediction in Temporal Knowledge Graphs
Ishaan Singh; Navdeep Kaur; Garima Gaur; Mausam;
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Highlight: In response, we propose a novel NS model for TKGC called NeuSTIP, which performs link prediction and time interval prediction in a TKG.
358, ZGUL: Zero-shot Generalization to Unseen Languages Using Multi-source Ensembling of Language Adapters
Vipul Rathore; Rajdeep Dhingra; Parag Singla; Mausam;
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Highlight: We tackle the problem of zero-shot cross-lingual transfer in NLP tasks via the use of language adapters (LAs).
359, TacoPrompt: A Collaborative Multi-Task Prompt Learning Method for Self-Supervised Taxonomy Completion
Hongyuan Xu; Ciyi Liu; Yuhang Niu; Yunong Chen; Xiangrui Cai; Yanlong Wen; Xiaojie Yuan;
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Highlight: To address the aforementioned limitations, we propose TacoPrompt, a Collaborative Multi-Task Prompt Learning Method for Self-Supervised Taxonomy Completion.
360, Non-Autoregressive Math Word Problem Solver with Unified Tree Structure
Yi Bin; Mengqun Han; Wenhao Shi; Lei Wang; Yang Yang; See-Kiong Ng; Heng Shen;
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Highlight: The multiple solution variants depicting different possible solving procedures for the same input problem would raise two issues: 1) making it hard for the model to learn the mapping function between the input and output spaces effectively, and 2) wrongly indicating wrong when evaluating a valid expression variant. To address these issues, we introduce a unified tree structure to present a solution expression, where the elements are permutable and identical for all the expression variants.
361, Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks
Heng Wang; Wenqian Zhang; Yuyang Bai; Zhaoxuan Tan; Shangbin Feng; Qinghua Zheng; Minnan Luo;
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Highlight: In light of these challenges, we first curate a large-scale network-based spoiler detection dataset LCS and a comprehensive and up-to-date movie knowledge base UKM. We then propose MVSD, a novel spoiler detection model that takes into account the external knowledge about movies and user activities on movie review platforms.
362, Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering
Fangkai Yang; Pu Zhao; Zezhong Wang; Lu Wang; Bo Qiao; Jue Zhang; Mohit Garg; Qingwei Lin; Saravan Rajmohan; Dongmei Zhang;
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Highlight: In this paper, we provide a benchmark Question Answering (QA) dataset named MSQA, centered around Microsoft products and IT technical problems encountered by customers.
363, TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models
Jing Xiong; Jianhao Shen; Ye Yuan; Haiming Wang; Yichun Yin; Zhengying Liu; Lin Li; Zhijiang Guo; Qingxing Cao; Yinya Huang; Chuanyang Zheng; Xiaodan Liang; Ming Zhang; Qun Liu;
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Highlight: In this work, we propose TRIGO, an ATP benchmark that not only requires a model to reduce a trigonometric expression with step-by-step proof but also evaluates a generative LM?s reasoning ability on formulas and capability to manipulate, group, and factor number terms.
364, IBADR: An Iterative Bias-Aware Dataset Refinement Framework for Debiasing NLU Models
Xiaoyue Wang; Xin Liu; Lijie Wang; Yaoxiang Wang; Jinsong Su; Hua Wu;
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Highlight: In this paper, we propose IBADR, an Iterative Bias-Aware Dataset Refinement framework, which debiases NLU models without predefining biased features.
365, The Curious Case of Hallucinatory (Un)answerability: Finding Truths in The Hidden States of Over-Confident Large Language Models
Aviv Slobodkin; Omer Goldman; Avi Caciularu; Ido Dagan; Shauli Ravfogel;
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Highlight: In this paper, we explore the behavior of LLMs when presented with (un)answerable queries.
366, Counting The Bugs in ChatGPT?s Wugs: A Multilingual Investigation Into The Morphological Capabilities of A Large Language Model
Leonie Weissweiler; Valentin Hofmann; Anjali Kantharuban; Anna Cai; Ritam Dutt; Amey Hengle; Anubha Kabra; Atharva Kulkarni; Abhishek Vijayakumar; Haofei Yu; Hinrich Schuetze; Kemal Oflazer; David Mortensen;
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Highlight: We apply a version of Berko?s (1958) wug test to ChatGPT, using novel, uncontaminated datasets for the four examined languages.
367, Bridging Background Knowledge Gaps in Translation with Automatic Explicitation
HyoJung Han; Jordan Boyd-Graber; Marine Carpuat;
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Highlight: This work introduces techniques for automatically generating explicitations, motivated by WikiExpl: a dataset that we collect from Wikipedia and annotate with human translators.
368, DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models
Chengcheng Han; Xiaowei Du; Che Zhang; Yixin Lian; Xiang Li; Ming Gao; Baoyuan Wang;
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Highlight: In this paper, we propose Dialogue-guided Chain-of-Thought (DialCoT) to improve the reasoning capabilities of SLMs, with the aim of generating intermediate reasoning steps in a dialogue format to guide the model to the final answer.
369, ALDi: Quantifying The Arabic Level of Dialectness of Text
Amr Keleg; Sharon Goldwater; Walid Magdy;
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Highlight: We introduce the AOC-ALDi dataset (derived from the AOC dataset), containing 127,835 sentences (17% from news articles and 83% from user comments on those articles) which are manually labeled with their level of dialectness.
370, Self-Improvement of Non-autoregressive Model Via Sequence-Level Distillation
Yusheng Liao; Shuyang Jiang; Yiqi Li; Yu Wang; Yanfeng Wang;
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Highlight: In this paper, we propose a method called Sequence-Level Self-Distillation (SLSD), which aims to generate distilled data by the NAT model itself, eliminating the need for additional teacher networks.
371, Contextual Interaction for Argument Post Quality Assessment
Yiran Wang; Xuanang Chen; Ben He; Le Sun;
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Highlight: By incorporating this approach, we aim to enhance the assessment of argument quality by effectively distinguishing between arguments with subtle differences in quality.
372, Improving Image Captioning Via Predicting Structured Concepts
Ting Wang; Weidong Chen; Yuanhe Tian; Yan Song; Zhendong Mao;
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Highlight: In this paper, we propose a structured concept predictor (SCP) to predict concepts and their structures, then we integrate them into captioning, so that enhance the contribution of visual signals in this task via concepts and further use their relations to distinguish cross-modal semantics for better description generation.
373, Air-Decoding: Attribute Distribution Reconstruction for Decoding-Time Controllable Text Generation
Tianqi Zhong; Quan Wang; Jingxuan Han; Yongdong Zhang; Zhendong Mao;
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Highlight: This limitation hinders the effectiveness of decoding methods in achieving high levels of controllability. To address this problem, we propose a novel lightweight decoding framework named Air-Decoding.
374, E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation
Fengyi Fu; Lei Zhang; Quan Wang; Zhendong Mao;
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Highlight: In this paper, we propose a novel emotion correlation enhanced empathetic dialogue generation framework, which comprehensively realizes emotion correlation learning, utilization, and supervising.
375, ATFormer: A Learned Performance Model with Transfer Learning Across Devices for Deep Learning Tensor Programs
Yang Bai; Wenqian Zhao; Shuo Yin; Zixiao Wang; Bei Yu;
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Highlight: This paper presents ATFormer, a simple yet efficient design with attention-inspired modules to accurately predict the performance of optimized operators by capturing global and long-range dependencies within a complete scheduling space.
376, Small Language Models Fine-tuned to Coordinate Larger Language Models Improve Complex Reasoning
Gurusha Juneja; Subhabrata Dutta; Soumen Chakrabarti; Sunny Manchanda; Tanmoy Chakraborty;
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Highlight: We introduce DaSLaM, which uses a decomposition generator to decompose complex problems into subproblems that require fewer reasoning steps.
377, Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning
Hao Zhao; Jie Fu; Zhaofeng He;
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Highlight: Despite the success, most existing methods independently adapt to each task without considering knowledge transfer between tasks and are limited to low-data regimes. To overcome this issue, we propose Prototype-based HyperAdapter (PHA), a novel framework built on the adapter-tuning and hypernetwork.
378, Large Language Models Can Self-Improve
Jiaxin Huang; Shixiang Gu; Le Hou; Yuexin Wu; Xuezhi Wang; Hongkun Yu; Jiawei Han;
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Highlight: In this work, we demonstrate that an LLM is also capable of self-improving with only unlabeled datasets.
379, The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions
Siru Ouyang; Shuohang Wang; Yang Liu; Ming Zhong; Yizhu Jiao; Dan Iter; Reid Pryzant; Chenguang Zhu; Heng Ji; Jiawei Han;
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Highlight: This paper provides a comprehensive analysis of the divergence between academic research in NLP and the needs of real-world NLP applications via a large-scale collection of user-GPT conversations.
380, GLEN: General-Purpose Event Detection for Thousands of Types
Sha Li; Qiusi Zhan; Kathryn Conger; Martha Palmer; Heng Ji; Jiawei Han;
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Highlight: To make event extraction systems more accessible, we build a general-purpose event detection dataset GLEN, which covers 205K event mentions with 3,465 different types, making it more than 20x larger in ontology than today?s largest event dataset.
381, PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training
Yunyi Zhang; Minhao Jiang; Yu Meng; Yu Zhang; Jiawei Han;
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Highlight: In this paper, we propose a new method, PIEClass, consisting of two modules: (1) a pseudo label acquisition module that uses zero-shot prompting of pre-trained language models (PLM) to get pseudo labels based on contextualized text understanding beyond static keyword matching, and (2) a noise-robust iterative ensemble training module that iteratively trains classifiers and updates pseudo labels by utilizing two PLM fine-tuning methods that regularize each other.
382, CombLM: Adapting Black-Box Language Models Through Small Fine-Tuned Models
Aitor Ormazabal; Mikel Artetxe; Eneko Agirre;
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Highlight: In this work, we present a lightweight method for adapting large LMs to new domains and tasks, assuming no access to their weights or intermediate activations.
383, Language Model Is Suitable for Correction of Handwritten Mathematical Expressions Recognition
Zui Chen; Jiaqi Han; Chaofan Yang; Yi Zhou;
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Highlight: This article investigates the distinctive language characteristics of LaTeX mathematical expressions, revealing two key observations: 1) the presence of explicit structural symbols, and 2) the treatment of symbols, particularly letters, as minimal units with context-dependent semantics, representing variables or constants. Rooted in these properties, we propose that language models have the potential to synchronously and complementarily provide both structural and semantic information, making them suitable for correction of HMER.
384, POE: Process of Elimination for Multiple Choice Reasoning
Chenkai Ma; Xinya Du;
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Highlight: To this end, we present the Process of Elimination (POE), a two-step scoring method.
385, A Systematic Study of Performance Disparities in Multilingual Task-Oriented Dialogue Systems
Songbo Hu; Han Zhou; Moy Yuan; Milan Gritta; Guchun Zhang; Ignacio Iacobacci; Anna Korhonen; Ivan Vulic;
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Highlight: Beyond providing a series of insights into the performance disparities of ToD systems in different languages, our analyses offer practical tips on how to approach ToD data collection and system development for new languages.
386, Program Translation Via Code Distillation
Yufan Huang; Mengnan Qi; Yongqiang Yao; Maoquan Wang; Bin Gu; Colin Clement; Neel Sundaresan;
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Highlight: In this paper we propose a novel model called Code Distillation (CoDist) whereby we capture the semantic and structural equivalence of code in a language agnostic intermediate representation.
387, SUT: Active Defects Probing for Transcompiler Models
Mengnan Qi; Yufan Huang; Maoquan Wang; Yongqiang Yao; Zihan Liu; Bin Gu; Colin Clement; Neel Sundaresan;
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Highlight: Metrics like BLUE, CodeBLUE and computation accuracy may not expose these issues. In this paper we introduce a new metrics for programming language translation and these metrics address these basic syntax errors.
388, MT2: Towards A Multi-Task Machine Translation Model with Translation-Specific In-Context Learning
Chunyou Li; Mingtong Liu; Hongxiao Zhang; Yufeng Chen; Jinan Xu; Ming Zhou;
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Highlight: Most of the previous work uses separate models or methods to solve these tasks, which is not conducive to knowledge transfer of different tasks and increases the complexity of system construction. In this work, we explore the potential of pre-trained language model in machine translation tasks and propose a Multi-Task Machine Translation (MT2) model to integrate these translation tasks.
389, Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Papers
Chen Tang; Shun Wang; Tomas Goldsack; Chenghua Lin;
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Highlight: We propose a novel attention-based citation aggregation model that integrates domain-specific knowledge from citation papers, allowing neural networks to generate summaries by leveraging both the paper content and relevant knowledge from citation papers.
390, Compressing Context to Enhance Inference Efficiency of Large Language Models
Yucheng Li; Bo Dong; Frank Guerin; Chenghua Lin;
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Highlight: This paper proposes a method called Selective Context that enhances the inference efficiency of LLMs by identifying and pruning redundancy in the input context to make the input more compact.
391, A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?
Aniket Pramanick; Yufang Hou; Saif Mohammad; Iryna Gurevych;
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Highlight: In this study, we propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques.
392, Learning from Mistakes Via Cooperative Study Assistant for Large Language Models
Danqing Wang; Lei Li;
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Highlight: In this paper, we propose Study Assistant for Large LAnguage Model (SALAM), a novel framework with an auxiliary agent to assist the main LLM in learning from mistakes through interactive cooperation.
393, ClusterLLM: Large Language Models As A Guide for Text Clustering
Yuwei Zhang; Zihan Wang; Jingbo Shang;
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Highlight: We introduce ClusterLLM, a novel text clustering framework that leverages feedback from an instruction-tuned large language model, such as ChatGPT.
394, Can Language Models Laugh at YouTube Short-form Videos?
Dayoon Ko; Sangho Lee; Gunhee Kim;
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Highlight: We curate a user-generated dataset of 10K multimodal funny videos from YouTube, called ExFunTube.
395, MRedditSum: A Multimodal Abstractive Summarization Dataset of Reddit Threads with Images
Keighley Overbay; Jaewoo Ahn; Fatemeh Pesaran zadeh; Joonsuk Park; Gunhee Kim;
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Highlight: To this end, we present mRedditSum, the first multimodal discussion summarization dataset.
396, PRESTO: A Multilingual Dataset for Parsing Realistic Task-Oriented Dialogs
Rahul Goel; Waleed Ammar; Aditya Gupta; Siddharth Vashishtha; Motoki Sano; Faiz Surani; Max Chang; HyunJeong Choe; David Greene; Chuan He; Rattima Nitisaroj; Anna Trukhina; Shachi Paul; Pararth Shah; Rushin Shah; Zhou Yu;
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Highlight: To enable research on some of the more challenging aspects of parsing realistic conversations, we introduce PRESTO, a public dataset of over 550K contextual multilingual conversations between humans and virtual assistants.
397, Condensing Multilingual Knowledge with Lightweight Language-Specific Modules
Haoran Xu; Weiting Tan; Shuyue Li; Yunmo Chen; Benjamin Van Durme; Philipp Koehn; Kenton Murray;
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Highlight: We present Language-specific Matrix Synthesis (LMS), a novel method that addresses the issue.
398, PROSE: A Pronoun Omission Solution for Chinese-English Spoken Language Translation
Ke Wang; Xiutian Zhao; Yanghui Li; Wei Peng;
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Highlight: To alleviate the negative impact introduced by pro-drop, we propose Mention-Aware Semantic Augmentation, a novel approach that leverages the semantic embedding of dropped pronouns to augment training pairs.
399, M3Seg: A Maximum-Minimum Mutual Information Paradigm for Unsupervised Topic Segmentation in ASR Transcripts
Ke Wang; Xiutian Zhao; Yanghui Li; Wei Peng;
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Highlight: In this work, we propose M3Seg, a novel Maximum-Minimum Mutual information paradigm for linear topic segmentation without using any parallel data.
400, GEMINI: Controlling The Sentence-Level Summary Style in Abstractive Text Summarization
Guangsheng Bao; Zebin Ou; Yue Zhang;
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Highlight: These techniques are flexible and thus difficult to be imitated by any single method. To address this issue, we propose an adaptive model, GEMINI, that integrates a rewriter and a generator to mimic the sentence rewriting and abstracting techniques, respectively.
401, Optimizing Retrieval-augmented Reader Models Via Token Elimination
Moshe Berchansky; Peter Izsak; Avi Caciularu; Ido Dagan; Moshe Wasserblat;
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Highlight: In this work, we analyze the contribution and necessity of all the retrieved passages to the performance of reader models, and propose eliminating some of the retrieved information, at the token level, that might not contribute essential information to the answer generation process.
402, Learning Retrieval Augmentation for Personalized Dialogue Generation
Qiushi Huang; Shuai Fu; Xubo Liu; Wenwu Wang; Tom Ko; Yu Zhang; Lilian Tang;
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Highlight: However, persona profiles, a prevalent setting in current personalized dialogue datasets, typically composed of merely four to five sentences, may not offer comprehensive descriptions of the persona about the agent, posing a challenge to generate truly personalized dialogues. To handle this problem, we propose Learning Retrieval Augmentation for Personalized DialOgue Generation (LAPDOG), which studies the potential of leveraging external knowledge for persona dialogue generation.
403, Critic-Driven Decoding for Mitigating Hallucinations in Data-to-text Generation
Mateusz Lango; Ondrej Dusek;
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Highlight: In this paper, we explore a new way to mitigate hallucinations by combining the probabilistic output of a generator language model (LM) with the output of a special ?text critic? classifier, which guides the generation by assessing the match between the input data and the text generated so far.
404, Image Manipulation Via Multi-Hop Instructions - A New Dataset and Weakly-Supervised Neuro-Symbolic Approach
Harman Singh; Poorva Garg; Mohit Gupta; Kevin Shah; Ashish Goswami; Satyam Modi; Arnab Mondal; Dinesh Khandelwal; Dinesh Garg; Parag Singla;
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Highlight: We create a new dataset for the task, and extensive experiments demonstrate that NeuroSIM is highly competitive with or beats SOTA baselines that make use of supervised data for manipulation.
405, Weakly-Supervised Learning of Visual Relations in Multimodal Pretraining
Emanuele Bugliarello; Aida Nematzadeh; Lisa Hendricks;
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Highlight: In this work, we take a step further and explore how we can tap into supervision from small-scale visual relation data.
406, Large Language Models Are Temporal and Causal Reasoners for Video Question Answering
Dohwan Ko; Ji Lee; Woo-Young Kang; Byungseok Roh; Hyunwoo Kim;
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Highlight: In this paper, we develop LLaMA-VQA by applying Flipped-VQA to LLaMA, and it outperforms both LLMs-based and non-LLMs-based models on five challenging VideoQA benchmarks.
407, KEBAP: Korean Error Explainable Benchmark Dataset for ASR and Post-processing
Seonmin Koo; Chanjun Park; Jinsung Kim; Jaehyung Seo; Sugyeong Eo; Hyeonseok Moon; Heuiseok Lim;
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Highlight: Conventional evaluation metrics for ASR systems produce a singular aggregate score, which is insufficient for understanding specific system vulnerabilities. Therefore, we aim to address the limitations of the previous ASR evaluation methods by introducing the Korean Error Explainable Benchmark Dataset for ASR and Post-processing (KEBAP).
408, Post-hoc Utterance Refining Method By Entity Mining for Faithful Knowledge Grounded Conversations
Yoonna Jang; Suhyune Son; Jeongwoo Lee; Junyoung Son; Yuna Hur; Jungwoo Lim; Hyeonseok Moon; Kisu Yang; Heuiseok Lim;
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Highlight: In particular, entity-level hallucination that causes critical misinformation and undesirable conversation is one of the major concerns. To address this issue, we propose a post-hoc refinement method called REM.
409, Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource Settings
Josip Jukic; Jan Snajder;
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Highlight: We present an empirical study of PEFT behavior with AL in low-resource settings for text classification tasks.
410, CHEF in The Language Kitchen: A Generative Data Augmentation Leveraging Korean Morpheme Ingredients
Jaehyung Seo; Hyeonseok Moon; Jaewook Lee; Sugyeong Eo; Chanjun Park; Heuiseok Lim;
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Highlight: The complexity of morphological variations allows for diverse sentence forms based on the syntactic-semantic integration of functional morphemes (i. e. , affixes) to lexical morphemes (i. e. , roots). With this in mind, we propose a method - CHEF, replicating the morphological transformations inherent in sentences based on lexical and functional morpheme combinations through generative data augmentation.
411, Comparing Styles Across Languages
Shreya Havaldar; Matthew Pressimone; Eric Wong; Lyle Ungar;
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Highlight: We introduce an explanation framework to extract stylistic differences from multilingual LMs and compare styles across languages.
412, Benchmarking and Improving Text-to-SQL Generation Under Ambiguity
Adithya Bhaskar; Tushar Tomar; Ashutosh Sathe; Sunita Sarawagi;
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Highlight: We propose LogicalBeam, a new decoding algorithm that navigates the SQL logic space using a blend of plan-based template generation and constrained infilling.
413, ReSee: Responding Through Seeing Fine-grained Visual Knowledge in Open-domain Dialogue
Haoqin Tu; Yitong Li; Fei Mi; Zhongliang Yang;
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Highlight: We propose to explicitly split the visual knowledge into finer granularity (?turn-level? and ?entity-level?).
414, Multilingual K-Nearest-Neighbor Machine Translation
David Stap; Christof Monz;
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Highlight: However, these improvements have been limited to high-resource language pairs, with large datastores, and remain a challenge for low-resource languages. In this paper, we address this issue by combining representations from multiple languages into a single datastore.
415, Make Every Example Count: On The Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets
Irina Bejan; Artem Sokolov; Katja Filippova;
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Highlight: We study the fitness of task-agnostic self-influence scores of training examples for data cleaning, analyze their efficacy in capturing naturally occurring outliers, and investigate to what extent self-influence based data cleaning can improve downstream performance in machine translation, question answering and text classification, building up on recent approaches to self-influence calculation and automated curriculum learning.
416, MarkQA: A Large Scale KBQA Dataset with Numerical Reasoning
Xiang Huang; Sitao Cheng; Yuheng Bao; Shanshan Huang; Yuzhong Qu;
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Highlight: In this paper, we focus on the complex numerical reasoning in KBQA, and propose a new task, NR-KBQA, which necessitates the ability to perform both multi-hop reasoning and numerical reasoning.
417, Learning to Rank Context for Named Entity Recognition Using A Synthetic Dataset
Arthur Amalvy; Vincent Labatut; Richard Dufour;
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Highlight: Instead, we propose to generate a synthetic context retrieval training dataset using Alpaca, an instruction-tuned large language model (LLM).
418, Paraphrase Types for Generation and Detection
Jan Philip Wahle; Bela Gipp; Terry Ruas;
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Highlight: Current approaches in paraphrase generation and detection heavily rely on a single general similarity score, ignoring the intricate linguistic properties of language. This paper introduces two new tasks to address this shortcoming by considering paraphrase types - specific linguistic perturbations at particular text positions.
419, DueT: Image-Text Contrastive Transfer Learning with Dual-adapter Tuning
Taku Hasegawa; Kyosuke Nishida; Koki Maeda; Kuniko Saito;
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Highlight: This paper presents DueT, a novel transfer learning method for vision and language models built by contrastive learning.
420, Empower Nested Boolean Logic Via Self-Supervised Curriculum Learning
Hongqiu Wu; Linfeng Liu; Hai Zhao; Min Zhang;
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Highlight: We find that any pre-trained language models even including large language models only behave like a random selector in the face of multi-nested boolean logic, a task that humans can handle with ease. To empower language models with this fundamental capability, this paper proposes a new self-supervised learning method Curriculum Logical Reasoning (Clr), where we augment the training data with nested boolean logic chain step-by-step, and program the training from simpler logical patterns gradually to harder ones.
421, Speech-enriched Memory for Inference-time Adaptation of ASR Models to Word Dictionaries
Ashish Mittal; Sunita Sarawagi; Preethi Jyothi; George Saon; Gakuto Kurata;
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Highlight: In this paper, we present a novel inference algorithm that improves the prediction of state-of-the-art ASR models using nearest-neighbor-based matching on an inference-time word list.
422, Dr ChatGPT Tell Me What I Want to Hear: How Different Prompts Impact Health Answer Correctness
Bevan Koopman; Guido Zuccon;
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Highlight: This paper investigates the significant impact different prompts have on the behaviour of ChatGPT when used for health information seeking.
423, ToolWriter: Question Specific Tool Synthesis for Tabular Data
Carlos Gemmell; Jeff Dalton;
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Highlight: Unlike humans who use programmatic tools like filters to transform data before processing, language models in TQA process tables directly, resulting in information loss as table size increases. In this paper we propose ToolWriter to generate query specific programs and detect when to apply them to transform tables and align them with the TQA model?s capabilities.
424, Large Language Models Are Complex Table Parsers
Bowen Zhao; Changkai Ji; Yuejie Zhang; Wen He; Yingwen Wang; Qing Wang; Rui Feng; Xiaobo Zhang;
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Highlight: In this paper, we propose to incorporate GPT-3. 5 to address such challenges, in which complex tables are reconstructed into tuples and specific prompt designs are employed for dialogues.
425, Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPT
Xiaoshuai Song; Keqing He; Pei Wang; Guanting Dong; Yutao Mou; Jingang Wang; Yunsen Xian; Xunliang Cai; Weiran Xu;
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Highlight: More deeply, through a series of analytical experiments, we summarize and discuss the challenges faced by LLMs including clustering, domain-specific understanding, and cross-domain in-context learning scenarios.
426, Chinese Lexical Substitution: Dataset and Method
Jipeng Qiang; Kang Liu; Ying Li; Yun Li; Yi Zhu; Yun-Hao Yuan; Xiaocheng Hu; Xiaoye Ouyang;
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Highlight: Existing lexical substitution (LS) benchmarks were collected by asking human annotators to think of substitutes from memory, resulting in benchmarks with limited coverage and relatively small scales. To overcome this problem, we propose a novel annotation method to construct an LS dataset based on human and machine collaboration.
427, Biomedical Named Entity Recognition Via Dictionary-based Synonym Generalization
Zihao Fu; Yixuan Su; Zaiqiao Meng; Nigel Collier;
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Highlight: In this study, we propose a novel Synonym Generalization (SynGen) framework that recognizes the biomedical concepts contained in the input text using span-based predictions.
428, Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning Via Compositional Operations
James Huang; Wenlin Yao; Kaiqiang Song; Hongming Zhang; Muhao Chen; Dong Yu;
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Highlight: We propose InterSent, an end-to-end framework for learning interpretable sentence embeddings that supports compositional sentence operations in the embedding space.
429, More Than Spoken Words: Nonverbal Message Extraction and Generation
Dian Yu; Xiaoyang Wang; Wanshun Chen; Nan Du; Longyue Wang; Haitao Mi; Dong Yu;
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Highlight: This paper introduces the task of extracting NMs in written text and generating NMs for spoken text.
430, Mitigating Backdoor Poisoning Attacks Through The Lens of Spurious Correlation
Xuanli He; Qiongkai Xu; Jun Wang; Benjamin Rubinstein; Trevor Cohn;
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Highlight: This paper posits that backdoor poisoning attacks exhibit a spurious correlation between simple text features and classification labels, and accordingly, proposes methods for mitigating spurious correlation as means of defence.
431, BUSTER: A ?BUSiness Transaction Entity Recognition? Dataset
Andrea Zugarini; Andrew Zamai; Marco Ernandes; Leonardo Rigutini;
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Highlight: To support industry-oriented research, we present BUSTER, a BUSiness Transaction Entity Recognition dataset.
432, Query-as-context Pre-training for Dense Passage Retrieval
Xing W; Guangyuan Ma; Wanhui Qian; Zijia Lin; Songlin Hu;
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Highlight: Thus, this paper proposes query-as-context pre-training, a simple yet effective pre-training technique to alleviate the issue.
433, CT-GAT: Cross-Task Generative Adversarial Attack Based on Transferability
Minxuan Lv; Chengwei Dai; Kun Li; Wei Zhou; Songlin Hu;
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Highlight: In this paper, we propose a novel approach that directly constructs adversarial examples by extracting transferable features across various tasks.
434, Rationale-Enhanced Language Models Are Better Continual Relation Learners
Weimin Xiong; Yifan Song; Peiyi Wang; Sujian Li;
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Highlight: To address the issue, we introduce rationale, i. e. , the explanations of relation classification results generated by Large Language Models (LLM), into CRE task.
435, Hierarchical Pretraining on Multimodal Electronic Health Records
Xiaochen Wang; Junyu Luo; Jiaqi Wang; Ziyi Yin; Suhan Cui; Yuan Zhong; Yaqing Wang; Fenglong Ma;
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Highlight: However, in the medical domain, existing pretrained models on electronic health records (EHR) fail to capture the hierarchical nature of EHR data, limiting their generalization capability across diverse downstream tasks using a single pretrained model. To tackle this challenge, this paper introduces a novel, general, and unified pretraining framework called MedHMP, specifically designed for hierarchically multimodal EHR data.
436, Identifying Informational Sources in News Articles
Alexander Spangher; Nanyun Peng; Emilio Ferrara; Jonathan May;
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Highlight: Modeling when, how and why sources get used together in stories can help us better understand the information we consume and even help journalists with the task of producing it. In this work, we take steps toward this goal by constructing the largest and widest-ranging annotated dataset, to date, of informational sources used in news writing.
437, A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation
Giuseppe Attanasio; Flor Plaza del Arco; Debora Nozza; Anne Lauscher;
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Highlight: In MT, this might lead to misgendered translations, resulting, among other harms, in the perpetuation of stereotypes and prejudices. In this work, we address this gap by investigating whether and to what extent such models exhibit gender bias in machine translation and how we can mitigate it.
438, Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis
Seraphina Goldfarb-Tarrant; Bj?rn Ross; Adam Lopez;
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Highlight: We also find racial biases to be much more prevalent than gender biases. To spur further research on this topic, we release the sentiment models we used for this study, and the intermediate checkpoints throughout training, yielding 1,525 distinct models; we also release our evaluation code.
439, Can You Follow Me? Testing Situational Understanding for ChatGPT
Chenghao Yang; Allyson Ettinger;
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Highlight: Previous works have identified certain SU limitations in non-chatbot Large Language models (LLMs), but the extent and causes of these limitations are not well understood, and capabilities of current chat-based models in this domain have not been explored. In this work we tackle these questions, proposing a novel synthetic environment for SU testing which allows us to do controlled and systematic testing of SU in chat-oriented models, through assessment of models? ability to track and enumerate environment states.
440, SimCSE++: Improving Contrastive Learning for Sentence Embeddings from Two Perspectives
Jiahao Xu; Wei Shao; Lihui Chen; Lemao Liu;
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Highlight: Specifically, for the first perspective, we identify that the dropout noise from negative pairs affects the model?s performance. Therefore, we propose a simple yet effective method to deal with such type of noise.
441, Question Answering As Programming for Solving Time-Sensitive Questions
Xinyu Zhu; Cheng Yang; Bei Chen; Siheng Li; Jian-Guang Lou; Yujiu Yang;
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Highlight: This can be attributed to the LLMs? inability to perform rigorous reasoning based on surface-level text semantics. To overcome this limitation, rather than requiring LLMs to directly answer the question, we propose a novel approach where we reframe the Question Answering task as Programming (QAaP).
442, Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective
Tianyu Liu; Afra Amini; Mrinmaya Sachan; Ryan Cotterell;
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Highlight: Such tasks, in general, require exhaustive pair-wise comparisons of tokens, thus having a quadratic runtime complexity in the length of the string. We show that these exhaustive comparisons can be avoided, and, moreover, the complexity of such tasks can be reduced to linear by casting the relation between tokens as a partial order over the string.
443, On The Representational Capacity of Recurrent Neural Language Models
Franz Nowak; Anej Svete; Li Du; Ryan Cotterell;
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Highlight: This work investigates the computational expressivity of language models (LMs) based on recurrent neural networks (RNNs).
444, Recurrent Neural Language Models As Probabilistic Finite-state Automata
Anej Svete; Ryan Cotterell;
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Highlight: However, LMs do not describe unweighted formal languages?rather, they define probability distributions over strings. In this work, we study what classes of such probability distributions RNN LMs can represent, which allows us to make more direct statements about their capabilities.
445, Analysing State-Backed Propaganda Websites: A New Dataset and Linguistic Study
Freddy Heppell; Kalina Bontcheva; Carolina Scarton;
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Highlight: The main contribution of this paper for the NLP community is in the novel dataset which enables studies of disinformation networks, and the training of NLP tools for disinformation detection.
446, A Challenging Multimodal Video Summary: Simultaneously Extracting and Generating Keyframe-Caption Pairs from Video
Keito Kudo; Haruki Nagasawa; Jun Suzuki; Nobuyuki Shimizu;
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Highlight: This paper proposes a practical multimodal video summarization task setting and a dataset to train and evaluate the task.
447, Natural Disaster Tweets Classification Using Multimodal Data
Mohammad Basit; Bashir Alam; Zubaida Fatima; Salman Shaikh;
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Highlight: In this paper, we have explored different models which can lead to the development of a system that deals with multimodal datasets and can perform sequential hierarchical classification.
448, DSI++: Updating Transformer Memory with New Documents
Sanket Mehta; Jai Gupta; Yi Tay; Mostafa Dehghani; Vinh Tran; Jinfeng Rao; Marc Najork; Emma Strubell; Donald Metzler;
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Highlight: In this work, we introduce DSI++, a continual learning challenge for DSI with the goal of continuously indexing new documents while being able to answer queries related to both previously and newly indexed documents.
449, Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions
Lucie-Aim?e Kaffee; Arnav Arora; Isabelle Augenstein;
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Highlight: Currently, only a few comments explicitly mention those policies ? 20% of the English ones, but as few as 2% of the German and Turkish comments. To aid in this process of understanding how content is moderated, we construct a novel multilingual dataset of Wikipedia editor discussions along with their reasoning in three languages.
450, From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning
Zheyuan Zhang; Shane Storks; Fengyuan Hu; Sungryull Sohn; Moontae Lee; Honglak Lee; Joyce Chai;
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Highlight: Cognitive psychology theorizes that humans are capable of utilizing fast and intuitive *heuristic* thinking to make decisions based on past experience, then rationalizing the decisions through slower and deliberative *analytic* reasoning. We incorporate these interlinked dual processes in fine-tuning and in-context learning with PLMs, applying them to two language understanding tasks that require coherent physical commonsense reasoning.
451, Explaining Interactions Between Text Spans
Sagnik Choudhury; Pepa Atanasova; Isabelle Augenstein;
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Highlight: Most notably, there is a lack of annotations capturing the human decision-making process with respect to the necessary interactions for informed decision-making in such tasks. To bridge this gap, we introduce SpanEx, a multi-annotator dataset of human span interaction explanations for two NLU tasks: NLI and FC.
452, CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks
Hoang Nguyen; Ye Liu; Chenwei Zhang; Tao Zhang; Philip Yu;
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Highlight: Motivated by multi-step reasoning of LLMs, we propose Coarse-to-Fine Chain-of-Thought (CoF-CoT) approach that breaks down NLU tasks into multiple reasoning steps where LLMs can learn to acquire and leverage essential concepts to solve tasks from different granularities.
453, Revisiting Automated Topic Model Evaluation with Large Language Models
Dominik Stammbach; Vil?m Zouhar; Alexander Hoyle; Mrinmaya Sachan; Elliott Ash;
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Highlight: Automatically evaluating their output and determining the optimal number of topics are both longstanding challenges, with no effective automated solutions to date. This paper proposes using large language models (LLMs) for these tasks.
454, Let GPT Be A Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation
Zhenwen Liang; Wenhao Yu; Tanmay Rajpurohit; Peter Clark; Xiangliang Zhang; Ashwin Kalyan;
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Highlight: In this paper, we present a novel approach for distilling math word problem solving capabilities from large language models (LLMs) into smaller, more efficient student models.
455, ToViLaG: Your Visual-Language Generative Model Is Also An Evildoer
Xinpeng Wang; Xiaoyuan Yi; Han Jiang; Shanlin Zhou; Zhihua Wei; Xing Xie;
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Highlight: On such a basis, we benchmarked the toxicity of a diverse spectrum of VLGMs and discovered that some models do more evil than expected while some are more vulnerable to infection, underscoring the necessity of VLGMs detoxification. Therefore, we develop an innovative bottleneck-based detoxification method.
456, Longtriever: A Pre-trained Long Text Encoder for Dense Document Retrieval
Junhan Yang; Zheng Liu; Chaozhuo Li; Guangzhong Sun; Xing Xie;
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Highlight: In this paper, a novel retrieval model Longtriever is proposed to embrace three core challenges of long document retrieval: substantial computational cost, incomprehensive document understanding, and scarce annotations.
457, Localizing Active Objects from Egocentric Vision with Symbolic World Knowledge
Te-Lin Wu; Yu Zhou; Nanyun Peng;
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Highlight: While existing works approach this problem from a pure vision perspective, we investigate to which extent the textual modality (i. e. , task instructions) and their interaction with visual modality can be beneficial. Specifically, we propose to improve phrase grounding models? ability on localizing the active objects by: (1) learning the role of ?objects undergoing change? and extracting them accurately from the instructions, (2) leveraging pre- and post-conditions of the objects during actions, and (3) recognizing the objects more robustly with descriptional knowledge.
458, Harnessing Black-Box Control to Boost Commonsense in LM?s Generation
Yufei Tian; Felix Zhang; Nanyun Peng;
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Highlight: In this paper, we present a computation-efficient framework that steers a frozen Pre-Trained Language Model (PTLM) towards more commonsensical generation (i. e. , producing a plausible output that incorporates a list of concepts in a meaningful way).
459, Gender Biases in Automatic Evaluation Metrics for Image Captioning
Haoyi Qiu; Zi-Yi Dou; Tianlu Wang; Asli Celikyilmaz; Nanyun Peng;
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Highlight: In this paper, we conduct a systematic study of gender biases in model-based automatic evaluation metrics for image captioning tasks.
460, ACQUIRED: A Dataset for Answering Counterfactual Questions In Real-Life Videos
Te-Lin Wu; Zi-Yi Dou; Qingyuan Hu; Yu Hou; Nischal Chandra; Marjorie Freedman; Ralph Weischedel; Nanyun Peng;
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Highlight: Among them, they only cover reasoning over synthetic environments or specific types of events (e. g. traffic collisions), making them hard to reliably benchmark the model generalization ability in diverse real-world scenarios and reasoning dimensions. To overcome these limitations, we develop a video question answering dataset, ACQUIRED: it consists of 3. 9K annotated videos, encompassing a wide range of event types and incorporating both first and third-person viewpoints, which ensures a focus on real-world diversity.
461, HyperNetwork-based Decoupling to Improve Model Generalization for Few-Shot Relation Extraction
Liang Zhang; Chulun Zhou; Fandong Meng; Jinsong Su; Yidong Chen; Jie Zhou;
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Highlight: By investigating the class separation of an FSRE model, we find that model upper layers are prone to learn relation-specific knowledge. Therefore, in this paper, we propose a HyperNetwork-based Decoupling approach to improve the generalization of FSRE models.
462, HutCRS: Hierarchical User-Interest Tracking for Conversational Recommender System
Mingjie Qian; Yongsen Zheng; Jinghui Qin; Liang Lin;
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Highlight: Furthermore, these methods assume that users like all attributes of the target item and dislike those unrelated to it, which can introduce bias in attribute-level feedback and impede the system?s ability to accurately identify the target item. To address these issues, we propose a more realistic, user-friendly, and explainable CRS framework called Hierarchical User-Interest Tracking for Conversational Recommender System (HutCRS).
463, Bias Neutralization in Non-Parallel Texts: A Cyclic Approach with Auxiliary Guidance
Karthic Madanagopal; James Caverlee;
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Highlight: Toward expanding the reach of bias neutralization, we propose in this paper a new approach called FairBalance.
464, Causal Reasoning Through Two Cognition Layers for Improving Generalization in Visual Question Answering
Trang Nguyen; Naoaki Okazaki;
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Highlight: Besides, diverse interpretations of the input lead to various modes of answer generation, highlighting the role of causal reasoning between interpreting and answering steps in VQA. Through this lens, we propose Cognitive pathways VQA (CopVQA) improving the multimodal predictions by emphasizing causal reasoning factors.
465, DocumentNet: Bridging The Data Gap in Document Pre-training
Lijun Yu; Jin Miao; Xiaoyu Sun; Jiayi Chen; Alexander Hauptmann; Hanjun Dai; Wei Wei;
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Highlight: In this paper, we propose a method to collect massive-scale and weakly labeled data from the web to benefit the training of VDER models.
466, Dual-Channel Span for Aspect Sentiment Triplet Extraction
Pan Li; Ping Li; Kai Zhang;
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Highlight: However, most of the existing span-based approaches suffer from enumerating all possible spans, since it can introduce too much noise in sentiment triplet extraction. To ease this burden, we propose a dual-channel span generation method to coherently constrain the search space of span candidates.
467, Augmenting Zero-Shot Dense Retrievers with Plug-in Mixture-of-Memories
Suyu Ge; Chenyan Xiong; Corby Rosset; Arnold Overwijk; Jiawei Han; Paul Bennett;
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Highlight: In this paper we improve the zero-shot generalization ability of language models via Mixture-Of-Memory Augmentation (MoMA), a mechanism that retrieves augmentation documents from multiple information corpora (external memories), with the option to ?plug in? unseen memory at inference time.
468, ?Fifty Shades of Bias?: Normative Ratings of Gender Bias in GPT Generated English Text
Rishav Hada; Agrima Seth; Harshita Diddee; Kalika Bali;
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Highlight: Specifically, we create the first dataset of GPT-generated English text with normative ratings of gender bias.
469, Unsupervised Grammatical Error Correction Rivaling Supervised Methods
Hannan Cao; Liping Yuan; Yuchen Zhang; Hwee Tou Ng;
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Highlight: In this paper, we employ the Break-It-Fix-It (BIFI) method to build an unsupervised GEC system.
470, Merging Generated and Retrieved Knowledge for Open-Domain QA
Yunxiang Zhang; Muhammad Khalifa; Lajanugen Logeswaran; Moontae Lee; Honglak Lee; Lu Wang;
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Highlight: Based on the intuition that answers supported by both sources are more likely to be correct, we propose COMBO, a Compatibility-Oriented knowledge Merging for Better Open-domain QA framework, to effectively leverage the two sources of information.
471, PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation
Gaurav Sahu; Olga Vechtomova; Dzmitry Bahdanau; Issam Laradji;
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Highlight: In this work, we propose a method to generate more helpful augmented data by utilizing the LLM?s abilities to follow instructions and perform few-shot classifications.
472, Transfer-Free Data-Efficient Multilingual Slot Labeling
Evgeniia Razumovskaia; Ivan Vulic; Anna Korhonen;
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Highlight: To mitigate the inherent data scarcity issue, current research on multilingual ToD assumes that sufficient English-language annotated data are always available for particular tasks and domains, and thus operates in a standard cross-lingual transfer setup. In this work, we depart from this often unrealistic assumption.
473, 4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees
Carlos G?mez-Rodr?guez; Diego Roca; David Vilares;
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Highlight: We introduce an encoding for parsing as sequence labeling that can represent any projective dependency tree as a sequence of 4-bit labels, one per word.
474, Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System
Weizhou Shen; Yingqi Gao; Canbin Huang; Fanqi Wan; Xiaojun Quan; Wei Bi;
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Highlight: In this paper, we propose the application of maximal marginal likelihood to train a perceptive retriever by utilizing signals from response generation for supervision.
475, Disentangling Transformer Language Models As Superposed Topic Models
Jia Peng Lim; Hady Lauw;
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Highlight: However, due to its hypothesised superposition properties, the final logits originating from the residual path are considered uninterpretable. Therefore, we posit that we can interpret TLM as superposed NTM by proposing a novel weight-based, model-agnostic and corpus-agnostic approach to search and disentangle decoder-only TLM, potentially mapping individual neurons to multiple coherent topics.
476, MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation
Zexue He; Yu Wang; An Yan; Yao Liu; Eric Chang; Amilcare Gentili; Julian McAuley; Chun-Nan Hsu;
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Highlight: In this paper, we present MedEval, a multi-level, multi-task, and multi-domain medical benchmark to facilitate the development of language models for healthcare.
477, Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings
Mattia Atzeni; Mikhail Plekhanov; Frederic Dreyer; Nora Kassner; Simone Merello; Louis Martin; Nicola Cancedda;
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Highlight: Entity linking methods based on dense retrieval are widely adopted in large-scale applications for their efficiency, but they can fall short of generative models, as they are sensitive to the structure of the embedding space. To address this issue, this paper introduces DUCK, an approach to infusing structural information in the space of entity representations, using prior knowledge of entity types.
478, Quantifying The Redundancy Between Prosody and Text
Lukas Wolf; Tiago Pimentel; Evelina Fedorenko; Ryan Cotterell; Alex Warstadt; Ethan Wilcox; Tamar Regev;
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Highlight: We use large language models (LLMs) to estimate how much information is redundant between prosody and the words themselves.
479, A Predictive Factor Analysis of Social Biases and Task-Performance in Pretrained Masked Language Models
Yi Zhou; Jose Camacho-Collados; Danushka Bollegala;
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Highlight: To study the relationship between model factors and the social biases learned by an MLM, as well as the downstream task performance of the model, we conduct a comprehensive study over 39 pretrained MLMs covering different model sizes, training objectives, tokenization methods, training data domains and languages.
480, Token Prediction As Implicit Classification to Identify LLM-Generated Text
Yutian Chen; Hao Kang; Vivian Zhai; Liangze Li; Rita Singh; Bhiksha Raj;
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Highlight: This paper introduces a novel approach for identifying the possible large language models (LLMs) involved in text generation.
481, Assessing Step-by-Step Reasoning Against Lexical Negation: A Case Study on Syllogism
Mengyu Ye; Tatsuki Kuribayashi; Jun Suzuki; Goro Kobayashi; Hiroaki Funayama;
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Highlight: In this study, we inspect the step-by-step reasoning ability of LLMs with a focus on negation, which is a core linguistic phenomenon that is difficult to process.
482, Efficient Transformer Knowledge Distillation: A Performance Review
Nathan Brown; Ashton Williamson; Tahj Anderson; Logan Lawrence;
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Highlight: In this work, we provide an evaluation of model compression via knowledge distillation on efficient attention transformers.
483, Text2Topic: Multi-Label Text Classification System for Efficient Topic Detection in User Generated Content with Zero-Shot Capabilities
Fengjun Wang; Moran Beladev; Ofri Kleinfeld; Elina Frayerman; Tal Shachar; Eran Fainman; Karen Lastmann Assaraf; Sarai Mizrachi; Benjamin Wang;
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Highlight: We propose Text to Topic (Text2Topic), which achieves high multi-label classification performance by employing a Bi-Encoder Transformer architecture that utilizes concatenation, subtraction, and multiplication of embeddings on both text and topic.
484, IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions
Zhebin Zhang; Xinyu Zhang; Yuanhang Ren; Saijiang Shi; Meng Han; Yongkang Wu; Ruofei Lai; Zhao Cao;
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Highlight: In this paper, we propose an Induction-Augmented Generation (IAG) framework that utilizes inductive knowledge along with the retrieved documents for implicit reasoning.
485, Introducing Rhetorical Parallelism Detection: A New Task with Datasets, Metrics, and Baselines
Stephen Bothwell; Justin DeBenedetto; Theresa Crnkovich; Hildegund Muller; David Chiang;
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Highlight: Despite the ubiquity of parallelism, the field of natural language processing has seldom investigated it, missing a chance to better understand the nature of the structure, meaning, and intent that humans convey. To address this, we introduce the task of rhetorical parallelism detection.
486, Efficient Algorithms for Recognizing Weighted Tree-Adjoining Languages
Alexandra Butoi; Tim Vieira; Ryan Cotterell; David Chiang;
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Highlight: These four formalisms are equivalent to tree-adjoining grammars (TAG), linear indexed grammars (LIG), pushdown-adjoining automata (PAA), and embedded pushdown automata (EPDA). We define semiring-weighted versions of the above two-level formalisms, and we design new algorithms for computing their stringsums (the weight of all derivations of a string) and allsums (the weight of all derivations).
487, Gatekeeper to Save COGS and Improve Efficiency of Text Prediction
Nidhi Tiwari; Sneha Kola; Milos Milunovic; Si-qing Chen; Marjan Slavkovski;
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Highlight: So, we propose a Model gatekeeper (GK) to stop the LLM calls that will result in incorrect predictions at client application level itself.
488, Revisiting Sparse Retrieval for Few-shot Entity Linking
Yulin Chen; Zhenran Xu; Baotian Hu; Min Zhang;
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Highlight: For training the extractor, we propose a distant supervision method to automatically generate training data based on overlapping tokens between mention contexts and entity descriptions.
489, QUDeval: The Evaluation of Questions Under Discussion Discourse Parsing
Yating Wu; Ritika Mangla; Greg Durrett; Junyi Jessy Li;
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Highlight: This work introduces the first framework for the automatic evaluation of QUD parsing, instantiating the theoretical constraints of QUD in a concrete protocol.
490, Elaborative Simplification As Implicit Questions Under Discussion
Yating Wu; William Sheffield; Kyle Mahowald; Junyi Jessy Li;
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Highlight: This view fails to account for elaborative simplification, where new information is added into the simplified text. This paper proposes to view elaborative simplification through the lens of the Question Under Discussion (QUD) framework, providing a robust way to investigate what writers elaborate upon, how they elaborate, and how elaborations fit into the discourse context by viewing elaborations as explicit answers to implicit questions.
491, Taxonomy Expansion for Named Entity Recognition
Karthikeyan K; Yogarshi Vyas; Jie Ma; Giovanni Paolini; Neha John; Shuai Wang; Yassine Benajiba; Vittorio Castelli; Dan Roth; Miguel Ballesteros;
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Highlight: However, this is an extremely laborious task. To remedy this, we propose a novel approach called Partial Label Model (PLM) that uses only partially annotated datasets.
492, Continual Named Entity Recognition Without Catastrophic Forgetting
Duzhen Zhang; Wei Cong; Jiahua Dong; Yahan Yu; Xiuyi Chen; Yonggang Zhang; Zhen Fang;
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Highlight: In this paper, we introduce a pooled feature distillation loss that skillfully navigates the trade-off between retaining knowledge of old entity types and acquiring new ones, thereby more effectively mitigating the problem of catastrophic forgetting.
493, Mirror: A Universal Framework for Various Information Extraction Tasks
Tong Zhu; Junfei Ren; Zijian Yu; Mengsong Wu; Guoliang Zhang; Xiaoye Qu; Wenliang Chen; Zhefeng Wang; Baoxing Huai; Min Zhang;
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Highlight: To this end, we reorganize IE problems into unified multi-slot tuples and propose a universal framework for various IE tasks, namely Mirror.
494, Text Rendering Strategies for Pixel Language Models
Jonas Lotz; Elizabeth Salesky; Phillip Rust; Desmond Elliott;
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Highlight: In this paper, we investigate four approaches to rendering text in the PIXEL model (Rust et al. , 2023), and find that simple character bigram rendering brings improved performance on sentence-level tasks without compromising performance on token-level or multilingual tasks.
495, CQE: A Comprehensive Quantity Extractor
Satya Almasian; Vivian Kazakova; Philipp G?ldner; Michael Gertz;
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Highlight: Compared to other information extraction approaches, interestingly only a few works exist that describe methods for a proper extraction and representation of quantities in text. In this paper, we present such a comprehensive quantity extraction framework from text data.
496, TLM: Token-Level Masking for Transformers
Yangjun Wu; Kebin Fang; Dongxiang Zhang; Han Wang; Hao Zhang; Gang Chen;
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Highlight: In this paper, we propose a new regularization scheme based on token-level rather than structure-level to reduce overfitting.
497, Prompting with Pseudo-Code Instructions
Mayank Mishra; Prince Kumar; Riyaz Bhat; Rudra Murthy; Danish Contractor; Srikanth Tamilselvam;
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Highlight: In this paper, we explore if prompting via pseudo-code instructions helps improve the performance of pre-trained language models.
498, Multilingual Simplification of Medical Texts
Sebastian Joseph; Kathryn Kazanas; Keziah Reina; Vishnesh Ramanathan; Wei Xu; Byron Wallace; Junyi Jessy Li;
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Highlight: We introduce MultiCochrane, the first sentence-aligned multilingual text simplification dataset for the medical domain in four languages: English, Spanish, French, and Farsi.
499, Towards Noise-Tolerant Speech-Referring Video Object Segmentation: Bridging Speech and Text
Xiang Li; Jinglu Wang; Xiaohao Xu; Muqiao Yang; Fan Yang; Yizhou Zhao; Rita Singh; Bhiksha Raj;
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Highlight: In this study, we investigate the prominent HCI task, Referring Video Object Segmentation (R-VOS), which aims to segment and track objects using linguistic references.
500, Reduce Human Labor On Evaluating Conversational Information Retrieval System: A Human-Machine Collaboration Approach
Chen Huang; Peixin Qin; Wenqiang Lei; Jiancheng Lv;
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Highlight: It is imperative to invest significant effort into researching more labor-effective methods for evaluating CIR systems. To touch upon this challenge, we take the first step to involve active testing in CIR evaluation and propose a novel method, called HomCoE.
501, Towards Effective Automatic Debt Collection with Persona Awareness
Tong Zhang; Junhong Liu; Chen Huang; Jia Liu; Hongru Liang; Zujie Wen; Wenqiang Lei;
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Highlight: In this paper, we take the first step towards comprehensively investigating the significance of debtor personas and present a successful commercial practice on automatic debt collection agents.
502, VLIS: Unimodal Language Models Guide Multimodal Language Generation
Jiwan Chung; Youngjae Yu;
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Highlight: However, existing vision-language models face challenges in tasks that require complex linguistic understanding. To address this issue, we introduce Visual-Language models as Importance Sampling weights (VLIS), a novel framework that combines the visual conditioning capability of vision-language models with the language understanding of unimodal text-only language models without further training.
503, Reading Books Is Great, But Not If You Are Driving! Visually Grounded Reasoning About Defeasible Commonsense Norms
Seungju Han; Junhyeok Kim; Jack Hessel; Liwei Jiang; Jiwan Chung; Yejin Son; Yejin Choi; Youngjae Yu;
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Highlight: We construct a new multimodal benchmark for studying commonsense norms: NormLens.
504, ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness
Jan Cegin; Jakub Simko; Peter Brusilovsky;
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Highlight: For some of these tasks, models like ChatGPT can potentially substitute human workers. In this study, we investigate whether this is the case for the task of paraphrase generation for intent classification.
505, Preserving Privacy Through Dememorization: An Unlearning Technique For Mitigating Memorization Risks In Language Models
Aly Kassem; Omar Mahmoud; Sherif Saad;
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Highlight: However, these methods have limitations regarding the number of protected samples, limited privacy types, and potentially lower-quality generative models. To tackle this challenge more effectively, we propose ?DeMem,? a novel unlearning approach that utilizes an efficient reinforcement learning feedback loop via proximal policy optimization.
506, CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction
Jingheng Ye; Yinghui Li; Qingyu Zhou; Yangning Li; Shirong Ma; Hai-Tao Zheng; Ying Shen;
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Highlight: However, mainstream evaluation metrics, i. e. , reference-based metrics, introduce bias into the multi-reference evaluation by extracting edits without considering the presence of multiple references. To overcome this issue, we propose Chunk-LE Multi-reference Evaluation (CLEME), designed to evaluate GEC systems in the multi-reference evaluation setting.
507, MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark
Dominik Macko; Robert Moro; Adaku Uchendu; Jason Lucas; Michiharu Yamashita; Mat?? Pikuliak; Ivan Srba; Thai Le; Dongwon Lee; Jakub Simko; Maria Bielikova;
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Highlight: This is also reflected in the available benchmarks which lack authentic texts in languages other than English and predominantly cover older generators. To fill this gap, we introduce MULTITuDE, a novel benchmarking dataset for multilingual machine-generated text detection comprising of 74,081 authentic and machine-generated texts in 11 languages (ar, ca, cs, de, en, es, nl, pt, ru, uk, and zh) generated by 8 multilingual LLMs.
508, Multi-Source Multi-Type Knowledge Exploration and Exploitation for Dialogue Generation
Xuanfan Ni; Hongliang Dai; Zhaochun Ren; Piji Li;
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Highlight: To harness the knowledge storage of LLMs, we propose a framework named KnowEE that explores multi-source multi-type knowledge from LLMs by leveraging diverse datasets and then exploits the obtained knowledge for response generation.
509, Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents
Jannis Vamvas; Rico Sennrich;
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Highlight: We formulate recognizing semantic differences (RSD) as a token-level regression task and study three unsupervised approaches that rely on a masked language model.
510, BRAINTEASER: Lateral Thinking Puzzles for Large Language Models
Yifan Jiang; Filip Ilievski; Kaixin Ma; Zhivar Sourati;
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Highlight: While such vertical thinking tasks have been relatively popular, lateral thinking puzzles have received little attention. To bridge this gap, we devise BrainTeaser: a multiple-choice Question Answering task designed to test the model?s ability to exhibit lateral thinking and defy default commonsense associations.
511, Pre-training Language Models for Comparative Reasoning
Mengxia Yu; Zhihan Zhang; Wenhao Yu; Meng Jiang;
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Highlight: In this paper, we propose a novel framework to pre-train language models for enhancing their abilities of comparative reasoning over texts.
512, Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation
Jian Wang; Yi Cheng; Dongding Lin; Chak Leong; Wenjie Li;
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Highlight: In this work, by formulating a pair as the conversation target, we explore a novel problem of personalized target-oriented dialogue by considering personalization during the target accomplishment process.
513, VIBE: Topic-Driven Temporal Adaptation for Twitter Classification
Yuji Zhang; Jing Li; Wenjie Li;
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Highlight: Most prior work focused on continued pretraining or knowledge updating, which may compromise their performance on noisy social media data. To tackle this issue, we reflect feature change via modeling latent topic evolution and propose a novel model, VIBE: Variational Information Bottleneck for Evolutions.
514, Self-Detoxifying Language Models Via Toxification Reversal
Chak Leong; Yi Cheng; Jiashuo Wang; Jian Wang; Wenjie Li;
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Highlight: In this paper, we propose a more lightweight approach that enables the PLM itself to achieve ?self-detoxification?.
515, Establishing Trustworthiness: Rethinking Tasks and Model Evaluation
Robert Litschko; Max M?ller-Eberstein; Rob van der Goot; Leon Weber-Genzel; Barbara Plank;
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Highlight: At the same time, LLMs are being deployed in more real-world scenarios, including previously unforeseen zero-shot setups, increasing the need for trustworthy and reliable systems. Therefore, we argue that it is time to rethink what constitutes tasks and model evaluation in NLP, and pursue a more holistic view on language, placing trustworthiness at the center.
516, ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation
Xinpeng Wang; Barbara Plank;
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Highlight: We show that in the active learning setting, a multi-head model performs significantly better than a single-head model in terms of uncertainty estimation.
517, LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models
Zhiqiang Hu; Lei Wang; Yihuai Lan; Wanyu Xu; Ee-Peng Lim; Lidong Bing; Xing Xu; Soujanya Poria; Roy Lee;
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Highlight: Among the various fine-tuning methods, adapter-based parameter-efficient fine-tuning (PEFT) is undoubtedly one of the most attractive topics, as it only requires fine-tuning a few external parameters instead of the entire LLMs while achieving comparable or even better performance. To enable further research on PEFT methods of LLMs, this paper presents LLM-Adapters, an easy-to-use framework that integrates various adapters into LLMs and can execute these adapter-based PEFT methods of LLMs for different tasks.
518, CoSyn: Detecting Implicit Hate Speech in Online Conversations Using A Context Synergized Hyperbolic Network
Sreyan Ghosh; Manan Suri; Purva Chiniya; Utkarsh Tyagi; Sonal Kumar; Dinesh Manocha;
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Highlight: In this paper, we present CoSyn, a context synergized neural network that explicitly incorporates user- and conversational-context for detecting implicit hate speech in online conversations.
519, Improving Dialogue Discourse Parsing Via Reply-to Structures of Addressee Recognition
Yaxin Fan; Feng Jiang; Peifeng Li; Fang Kong; Qiaoming Zhu;
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Highlight: To alleviate data sparsity, previous studies have adopted multitasking approaches to jointly learn dialogue discourse parsing with related tasks (e. g. , reading comprehension) that require additional human annotation, thus limiting their generality. In this paper, we propose a multitasking framework that integrates dialogue discourse parsing with its neighboring task addressee recognition.
520, DALE: Generative Data Augmentation for Low-Resource Legal NLP
Sreyan Ghosh; Chandra Kiran Reddy Evuru; Sonal Kumar; S Ramaneswaran; S Sakshi; Utkarsh Tyagi; Dinesh Manocha;
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Highlight: We present DALE, a novel and effective generative Data Augmentation framework for low-resource LEgal NLP.
521, APoLLo : Unified Adapter and Prompt Learning for Vision Language Models
Sanjoy Chowdhury; Sayan Nag; Dinesh Manocha;
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Highlight: We present APoLLo, a unified multi-modal approach that combines Adapter and Prompt learning for Vision-Language models.
522, Video-Helpful Multimodal Machine Translation
Yihang Li; Shuichiro Shimizu; Chenhui Chu; Sadao Kurohashi; Wei Li;
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Highlight: We introduce EVA (Extensive training set and Video-helpful evaluation set for Ambiguous subtitles translation), an MMT dataset containing 852k Japanese-English parallel subtitle pairs, 520k Chinese-English parallel subtitle pairs, and corresponding video clips collected from movies and TV episodes.
523, From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues
Shivani Kumar; Ramaneswaran S; Md Akhtar; Tanmoy Chakraborty;
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Highlight: Recognizing that emotional intelligence encompasses a comprehension of worldly knowledge, we propose an innovative approach that integrates commonsense information with dialogue context to facilitate a deeper understanding of emotions.
524, Learning Multilingual Sentence Representations with Cross-lingual Consistency Regularization
Pengzhi Gao; Liwen Zhang; Zhongjun He; Hua Wu; Haifeng Wang;
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Highlight: In this paper, we introduce MuSR: a one-for-all Multilingual Sentence Representation model that supports 223 languages.
525, Understanding Compositional Data Augmentation in Typologically Diverse Morphological Inflection
Farhan Samir; Miikka Silfverberg;
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Highlight: In this study, we aim to shed light on the theoretical aspects of the data augmentation strategy StemCorrupt, a method that generates synthetic examples by randomly substituting stem characters in existing gold standard training examples.
526, What Else Do I Need to Know? The Effect of Background Information on Users? Reliance on QA Systems
Navita Goyal; Eleftheria Briakou; Amanda Liu; Connor Baumler; Claire Bonial; Jeffrey Micher; Clare Voss; Marine Carpuat; Hal Daum? III;
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Highlight: In this work, we study how users interact with QA systems in the absence of sufficient information to assess their predictions.
527, Prompting Is Not A Substitute for Probability Measurements in Large Language Models
Jennifer Hu; Roger Levy;
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Highlight: In this study, we compare metalinguistic prompting and direct probability measurements as ways of measuring models? linguistic knowledge.
528, End-to-End Single-Channel Speaker-Turn Aware Conversational Speech Translation
Juan Pablo Zuluaga-Gomez; Zhaocheng Huang; Xing Niu; Rohit Paturi; Sundararajan Srinivasan; Prashant Mathur; Brian Thompson; Marcello Federico;
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Highlight: In this paper, we tackle single-channel multi-speaker conversational ST with an end-to-end and multi-task training model, named Speaker-Turn Aware Conversational Speech Translation, that combines automatic speech recognition, speech translation and speaker turn detection using special tokens in a serialized labeling format.
529, Hidding The Ghostwriters: An Adversarial Evaluation of AI-Generated Student Essay Detection
Xinlin Peng; Ying Zhou; Ben He; Le Sun; Yingfei Sun;
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Highlight: Although several detectors have been proposed to address these concerns, their effectiveness against adversarial perturbations, specifically in the context of student essay writing, remains largely unexplored. This paper aims to bridge this gap by constructing AIG-ASAP, an AI-generated student essay dataset, employing a range of text perturbation methods that are expected to generate high-quality essays while evading detection.
530, CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation
Philipp Borchert; Jochen De Weerdt; Kristof Coussement; Arno De Caigny; Marie-Francine Moens;
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Highlight: We introduce CORE, a dataset for few-shot relation classification (RC) focused on company relations and business entities.
531, What to Read in A Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions
Abhilasha Sancheti; Aparna Garimella; Balaji Srinivasan; Rachel Rudinger;
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Highlight: In this work, we propose a new task of party-specific extractive summarization for legal contracts to facilitate faster reviewing and improved comprehension of rights and duties.
532, Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling
Hai Yu; Chong Deng; Qinglin Zhang; Jiaqing Liu; Qian Chen; Wen Wang;
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Highlight: Therefore, this paper enhances the ability of supervised models to capture coherence from both logical structure and semantic similarity perspectives to further improve the topic segmentation performance, proposing Topic-aware Sentence Structure Prediction (TSSP) and Contrastive Semantic Similarity Learning (CSSL).
533, LLM4Vis: Explainable Visualization Recommendation Using ChatGPT
Lei Wang; Songheng Zhang; Yun Wang; Ee-Peng Lim; Yong Wang;
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Highlight: To address this research gap, we propose LLM4Vis, a novel ChatGPT-based prompting approach to perform visualization recommendation and return human-like explanations using very few demonstration examples.
534, Enhancing Computation Efficiency in Large Language Models Through Weight and Activation Quantization
Janghwan Lee; Minsoo Kim; Seungcheol Baek; Seok Hwang; Wonyong Sung; Jungwook Choi;
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Highlight: We present two innovative techniques: activation-quantization-aware scaling (AQAS) and sequence-length-aware calibration (SLAC) to enhance PTQ by considering the combined effects on weights and activations and aligning calibration sequence lengths to target tasks.
535, Effects of Sub-word Segmentation on Performance of Transformer Language Models
Jue Hou; Anisia Katinskaia; Anh-Duc Vu; Roman Yangarber;
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Highlight: In this paper, we compare GPT and BERT models trained with the statistical segmentation algorithm BPE vs. two unsupervised algorithms for morphological segmentation ? Morfessor and StateMorph.
536, Understanding The Effect of Model Compression on Social Bias in Large Language Models
Gustavo Gon?alves; Emma Strubell;
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Highlight: We perform a carefully controlled study of the impact of model compression via quantization and knowledge distillation on measures of social bias in LLMs.
537, Unraveling Feature Extraction Mechanisms in Neural Networks
Xiaobing Sun; Jiaxi Li; Wei Lu;
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Highlight: In this work, we propose a theoretical approach based on Neural Tangent Kernels (NTKs) to investigate such mechanisms.
538, To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing
Sireesh Gururaja; Amanda Bertsch; Clara Na; David Widder; Emma Strubell;
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Highlight: In this work, we seek to understand how to shape our future by better understanding our past.
539, MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments
Debtanu Datta; Shubham Soni; Rajdeep Mukherjee; Saptarshi Ghosh;
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Highlight: While prior research primarily focuses on summarizing legal case judgments in their source languages, this study presents a pioneering effort toward cross-lingual summarization of English legal documents into Hindi, the most frequently spoken Indian language.
540, Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model
Zeyu Liu; Tim Dettmers; Xi Lin; Veselin Stoyanov; Xian Li;
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Highlight: In this work, we analyzed two major design choices of S-FFN: the memory block (a. k. a. expert) size and the memory block selection method under a general conceptual framework of sparse neural memory.
541, Learning The Visualness of Text Using Large Vision-Language Models
Gaurav Verma; Ryan Rossi; Christopher Tensmeyer; Jiuxiang Gu; Ani Nenkova;
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Highlight: To this end, we curate a dataset of 3,620 English sentences and their visualness scores provided by multiple human annotators. We also propose a fine-tuning strategy that adapts large vision-language models like CLIP by modifying the model?s contrastive learning objective to map text identified as non-visual to a common NULL image while matching visual text to their corresponding images in the document.
542, Solving Hard Analogy Questions with Relation Embedding Chains
Nitesh Kumar; Steven Schockaert;
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Highlight: A common strategy is to rely on knowledge graphs (KGs) such as ConceptNet, and to model the relation between two concepts as a set of paths. However, KGs are limited to a fixed set of relation types, and they are incomplete and often noisy. Another strategy is to distill relation embeddings from a fine-tuned language model. However, this is less suitable for words that are only indirectly related and it does not readily allow us to incorporate structured domain knowledge. In this paper, we aim to combine the best of both worlds
543, Revisiting Block-based Quantisation: What Is Important for Sub-8-bit LLM Inference?
Cheng Zhang; Jianyi Cheng; Ilia Shumailov; George Constantinides; Yiren Zhao;
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Highlight: In this work, we explore the statistical and learning properties of the LLM layer and attribute the bottleneck of LLM quantisation to numerical scaling offsets.
544, M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis
Fei Zhao; Chunhui Li; Zhen Wu; Yawen Ouyang; Jianbing Zhang; Xinyu Dai;
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Highlight: Although some work attempts to filter low-quality noise images by setting thresholds, relying on thresholds will inevitably filter out a lot of useful image information. Therefore, in this work, we focus on whether the negative impact of noisy images can be reduced without modifying the data.
545, AdapterDistillation: Non-Destructive Task Composition with Knowledge Distillation
Junjie Wang; Yicheng Chen; Wangshu Zhang; Sen Hu; Teng Xu; Jing Zheng;
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Highlight: However, adding an extra fusion layer to implement knowledge composition not only increases the inference time but also is non-scalable for some applications. To avoid these issues, we propose a two-stage knowledge distillation algorithm called AdapterDistillation.
546, Beyond Shared Vocabulary: Increasing Representational Word Similarities Across Languages for Multilingual Machine Translation
Di Wu; Christof Monz;
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Highlight: However, when words overlap is small, e. g. , using different writing systems, transfer is inhibited. In this paper, we propose a re-parameterized method for building embeddings to alleviate this problem.
547, Bootstrapping Small & High Performance Language Models with Unmasking-Removal Training Policy
Yahan Yang; Elior Sulem; Insup Lee; Dan Roth;
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Highlight: BabyBERTa, a language model trained on small-scale child-directed speech while none of the words are unmasked during training, has been shown to achieve a level of grammaticality comparable to that of RoBERTa-base, which is trained on 6,000 times more words and 15 times more parameters. Relying on this promising result, we explore in this paper the performance of BabyBERTa-based models in downstream tasks, focusing on Semantic Role Labeling (SRL) and two Extractive Question Answering tasks, with the aim of building more efficient systems that rely on less data and smaller models.
548, TOD-Flow: Modeling The Structure of Task-Oriented Dialogues
Sungryull Sohn; Yiwei Lyu; Anthony Liu; Lajanugen Logeswaran; Dong-Ki Kim; Dongsub Shim; Honglak Lee;
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Highlight: While recent advances have capitalized on pre-trained language models (PLMs), they exhibit limitations regarding transparency and controllability. To address these challenges, we propose a novel approach focusing on inferring the TOD-flow graph from dialogue data annotated with dialog acts, uncovering the underlying task structure in the form of a graph.
549, Are All Steps Equally Important? Benchmarking Essentiality Detection in Event Processes
Haoyu Wang; Hongming Zhang; Yueguan Wang; Yuqian Deng; Muhao Chen; Dan Roth;
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Highlight: A critical but overlooked challenge in understanding an event process lies in the fact that the step events are not equally important to the central goal. In this paper, we seek to fill this gap by studying how well current models can understand the essentiality of different step events towards a goal event.
550, Adaptive Policy with Wait-k Model for Simultaneous Translation
Libo Zhao; Kai Fan; Wei Luo; Wu Jing; Shushu Wang; Ziqian Zeng; Zhongqiang Huang;
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Highlight: In this study, we propose a more flexible approach by decoupling the adaptive policy model from the translation model.
551, Training Simultaneous Speech Translation with Robust and Random Wait-k-Tokens Strategy
Linlin Zhang; Kai Fan; Jiajun Bu; Zhongqiang Huang;
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Highlight: Subsequently, to optimize the SimulST task, we propose a robust and random wait-k-tokens strategy.
552, Comparing Biases and The Impact of Multilingual Training Across Multiple Languages
Sharon Levy; Neha John; Ling Liu; Yogarshi Vyas; Jie Ma; Yoshinari Fujinuma; Miguel Ballesteros; Vittorio Castelli; Dan Roth;
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Highlight: We present a bias analysis across Italian, Chinese, English, Hebrew, and Spanish on the downstream sentiment analysis task to observe whether specific demographics are viewed more positively.
553, CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Low Resource With Contrastive Learning
Xiaoming Liu; Zhaohan Zhang; Yichen Wang; Hang Pu; Yu Lan; Chao Shen;
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Highlight: In this paper, we present a coherence-based contrastive learning model named CoCo to detect the possible MGT under low-resource scenario.
554, A Picture Is Worth A Thousand Words: Language Models Plan from Pixels
Anthony Liu; Lajanugen Logeswaran; Sungryull Sohn; Honglak Lee;
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Highlight: In this work, we explore the use of pre-trained language models (PLMs) to reason about plan sequences from text instructions in embodied visual environments.
555, Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding
Zheng Chen; Ziyan Jiang; Fan Yang; Eunah Cho; Xing Fan; Xiaojiang Huang; Yanbin Lu; Aram Galstyan;
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Highlight: This paper introduces our Collaborative Query Rewriting approach, which utilizes underlying topological information to assist in rewriting defective queries arising from unseen user interactions.
556, Failures Pave The Way: Enhancing Large Language Models Through Tuning-free Rule Accumulation
Zeyuan Yang; Peng Li; Yang Liu;
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Highlight: In this work, we propose our Tuning-free Rule Accumulation (TRAN) framework, which guides LLMs in improving their performance by learning from previous mistakes.
557, Learn and Consolidate: Continual Adaptation for Zero-Shot and Multilingual Neural Machine Translation
Kaiyu Huang; Peng Li; Junpeng Liu; Maosong Sun; Yang Liu;
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Highlight: To this end, we propose a two-stage approach that encourages original models to acquire language-agnostic multilingual representations from new data, and preserves the model architecture without introducing parameters.
558, MeaeQ: Mount Model Extraction Attacks with Efficient Queries
Chengwei Dai; Minxuan Lv; Kun Li; Wei Zhou;
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Highlight: However, these methods often result in selected queries that lack task relevance and data diversity, leading to limited success in achieving satisfactory results with low query costs. In this paper, we propose MeaeQ (Model extraction attack with efficient Queries), a straightforward yet effective method to address these issues.
559, Grounding Visual Illusions in Language: Do Vision-Language Models Perceive Illusions Like Humans?
Yichi Zhang; Jiayi Pan; Yuchen Zhou; Rui Pan; Joyce Chai;
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Highlight: Our findings have shown that although the overall alignment is low, larger models are closer to human perception and more susceptible to visual illusions.
560, Clustering Pseudo Language Family in Multilingual Translation Models with Fisher Information Matrix
Xinyu Ma; Xuebo Liu; Min Zhang;
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Highlight: Nevertheless, clustering languages based solely on their ancestral families can yield suboptimal results due to variations in the datasets employed during the model?s training phase. To mitigate this challenge, we introduce an innovative method that leverages the fisher information matrix (FIM) to cluster language families, anchored on the multilingual translation model?s characteristics.
561, Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations
Zhuoyan Li; Hangxiao Zhu; Zhuoran Lu; Ming Yin;
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Highlight: However, the effectiveness of the LLM-generated synthetic data in supporting model training is inconsistent across different classification tasks. To better understand factors that moderate the effectiveness of the LLM-generated synthetic data, in this study, we look into how the performance of models trained on these synthetic data may vary with the subjectivity of classification.
562, Faithful Model Evaluation for Model-Based Metrics
Qian Hu; Palash Goyal; Rahul Gupta;
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Highlight: In this work, we establish the mathematical foundation of significance testing for model-based metrics.
563, Coordinated Replay Sample Selection for Continual Federated Learning
Jack Good; Jimit Majmudar; Christophe Dupuy; Jixuan Wang; Charith Peris; Clement Chung; Richard Zemel; Rahul Gupta;
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Highlight: While replay-based algorithms that keep a small pool of past training data are effective to reduce forgetting, only simple replay sample selection strategies have been applied to CFL in prior work, and no previous work has explored coordination among clients for better sample selection. To bridge this gap, we adapt a replay sample selection objective based on loss gradient diversity to CFL and propose a new relaxation-based selection of samples to optimize the objective.
564, Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models
Pierre Colombo; Victor Pellegrain; Malik Boudiaf; Myriam Tami; Victor Storchan; Ismail Ayed; Pablo Piantanida;
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Highlight: First, we introduce a scenario where the embedding of a pre-trained model is served through a gated API with compute-cost and data-privacy constraints. Second, we propose a transductive inference, a learning paradigm that has been overlooked by the NLP community.
565, Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications
Manuel Faysse; Gautier Viaud; C?line Hudelot; Pierre Colombo;
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Highlight: Instruction Fine-Tuning (IFT) is a powerful paradigm that strengthens the zero-shot capabilities of Large Language Models (LLMs), but in doing so induces new evaluation metric requirements. We show LLM-based metrics to be well adapted to these requirements, and leverage them to conduct an investigation of task-specialization strategies, quantifying the trade-offs that emerge in practical industrial settings.
566, From Dissonance to Insights: Dissecting Disagreements in Rationale Construction for Case Outcome Classification
Shanshan Xu; Santosh T.y.s.s; Oana Ichim; Isabella Risini; Barbara Plank; Matthias Grabmair;
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Highlight: We hence collect a novel dataset RaVE: Rationale Variation in ECHR, which is obtained from two experts in the domain of international human rights law, for whom we observe weak agreement.
567, VECHR: A Dataset for Explainable and Robust Classification of Vulnerability Type in The European Court of Human Rights
Shanshan Xu; Leon Staufer; Santosh T.y.s.s; Oana Ichim; Corina Heri; Matthias Grabmair;
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Highlight: However, the concept of vulnerability remains elusive at the ECtHR and no prior NLP research has dealt with it. To enable future research in this area, we present VECHR, a novel expert-annotated multi-label dataset comprising of vulnerability type classification and explanation rationale.
568, Chain-of-Thought Tuning: Masked Language Models Can Also Think Step By Step in Natural Language Understanding
Caoyun Fan; Jidong Tian; Yitian Li; Wenqing Chen; Hao He; Yaohui Jin;
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Highlight: To migrate CoT from LLMs to MLMs, we propose Chain-of-Thought Tuning (CoTT), a two-step reasoning framework based on prompt tuning, to implement step-by-step thinking for MLMs on NLU tasks.
569, AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot Classification
Yongxin Huang; Kexin Wang; Sourav Dutta; Raj Patel; Goran Glava?; Iryna Gurevych;
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Highlight: As a solution, we propose AdaSent, which decouples SEPT from DAPT by training a SEPT adapter on the base PLM.
570, CiteBench: A Benchmark for Scientific Citation Text Generation
Martin Funkquist; Ilia Kuznetsov; Yufang Hou; Iryna Gurevych;
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Highlight: Yet, existing studies are based upon widely diverging task definitions, which makes it hard to study this task systematically. To address this challenge, we propose CiteBench: a benchmark for citation text generation that unifies multiple diverse datasets and enables standardized evaluation of citation text generation models across task designs and domains.
571, Exploring Jiu-Jitsu Argumentation for Writing Peer Review Rebuttals
Sukannya Purkayastha; Anne Lauscher; Iryna Gurevych;
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Highlight: In this work, we are the first to explore Jiu-Jitsu argumentation for peer reviews by proposing the novel task of attitude and theme-guided rebuttal generation.
572, Semantic Similarity Models for Depression Severity Estimation
Anxo P?rez; Neha Warikoo; Kexin Wang; Javier Parapar; Iryna Gurevych;
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Highlight: This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings.
573, Learning From Free-Text Human Feedback ? Collect New Datasets Or Extend Existing Ones?
Dominic Petrak; Nafise Moosavi; Ye Tian; Nikolai Rozanov; Iryna Gurevych;
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Highlight: However, for research on methods for learning from such data, annotated data is scarce. To address this, we examine the error and user response types of six popular dialogue datasets from various types, including MultiWoZ, PersonaChat, Wizards-of-Wikipedia, and others, to assess their extendibility with the needed annotations.
574, MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation
Jia-Chen Gu; Chao-Hong Tan; Caiyuan Chu; Zhen-Hua Ling; Chongyang Tao; Quan Liu; Cong Liu;
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Highlight: To study the scarcity of addressee labels which is a common issue in MPCs, we propose MADNet that maximizes addressee deduction expectation in heterogeneous graph neural networks for MPC generation.
575, PROMINET: Prototype-based Multi-View Network for Interpretable Email Response Prediction
Yuqing Wang; Prashanth Vijayaraghavan; Ehsan Degan;
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Highlight: This study proposes a Prototype-based Multi-view Network (PROMINET) that incorporates semantic and structural information from email data.
576, A Self-training Framework for Automated Medical Report Generation
Siyuan Wang; Zheng Liu; Bo Peng;
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Highlight: To this end, in this paper, we introduce a self-training framework named REMOTE (i. e. , Revisiting sElf-training for Medical repOrT gEneration) to exploit the unlabeled medical images and a reference-free evaluation metric MedCLIPScore to augment a small-scale medical report generation dataset for training accurate medical report generation model.
577, Standardizing Distress Analysis: Emotion-Driven Distress Identification and Cause Extraction (DICE) in Multimodal Online Posts
Gopendra Singh; Soumitra Ghosh; Atul Verma; Chetna Painkra; Asif Ekbal;
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Highlight: In this work, we present a novel problem of Distress Identification and Cause Extraction (DICE) from multimodal online posts.
578, Elevating Code-mixed Text Handling Through Auditory Information of Words
Mamta Mamta; Zishan Ahmad; Asif Ekbal;
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Highlight: In this paper, we propose an effective approach for creating language models for handling code-mixed textual data using auditory information of words from SOUNDEX.
579, When Reviewers Lock Horns: Finding Disagreements in Scientific Peer Reviews
Sandeep Kumar; Tirthankar Ghosal; Asif Ekbal;
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Highlight: Here in this work, we introduce a novel task of automatically identifying contradictions among reviewers on a given article.
580, DiNeR: A Large Realistic Dataset for Evaluating Compositional Generalization
Chengang Hu; Xiao Liu; Yansong Feng;
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Highlight: To better investigate compositional generalization with more linguistic phenomena and compositional diversity, we propose the DIsh NamE Recognition (DiNeR) task and create a large realistic Chinese dataset.
581, Improving Unsupervised Relation Extraction By Augmenting Diverse Sentence Pairs
Qing Wang; Kang Zhou; Qiao Qiao; Yuepei Li; Qi Li;
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Highlight: In this paper, we propose AugURE with both within-sentence pairs augmentation and augmentation through cross-sentence pairs extraction to increase the diversity of positive pairs and strengthen the discriminative power of contrastive learning.
582, CoRec: An Easy Approach for Coordination Recognition
Qing Wang; Haojie Jia; Wenfei Song; Qi Li;
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Highlight: In this paper, we observe and address the challenges of the coordination recognition task.
583, OssCSE: Overcoming Surface Structure Bias in Contrastive Learning for Unsupervised Sentence Embedding
Zhan Shi; Guoyin Wang; Ke Bai; Jiwei Li; Xiang Li; Qingjun Cui; Belinda Zeng; Trishul Chilimbi; Xiaodan Zhu;
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Highlight: In this paper, we first verify the bias by collecting a sentence transformation testset. Then we systematically probe the existing models by proposing novel splits based on benchmark datasets in accordance with semantic and surface structure similarity.
584, QA-NatVer: Question Answering for Natural Logic-based Fact Verification
Rami Aly; Marek Strong; Andreas Vlachos;
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Highlight: To this end, we propose to use question answering to predict natural logic operators, taking advantage of the generalization capabilities of instruction-tuned language models.
585, Faster Minimum Bayes Risk Decoding with Confidence-based Pruning
Julius Cheng; Andreas Vlachos;
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Highlight: We describe an algorithm for MBR which gradually grows the number of samples used to estimate the utility while pruning hypotheses that are unlikely to have the highest utility according to confidence estimates obtained with bootstrap sampling.
586, Automated Fact-Checking in Dialogue: Are Specialized Models Needed?
Eric Chamoun; Marzieh Saeidi; Andreas Vlachos;
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Highlight: However, creating separate models for each use case is impractical, and we show that fine-tuning models for dialogue results in poor performance on typical fact-checking. To overcome this challenge, we present techniques that allow us to use the same models for both dialogue and typical fact-checking.
587, SciRepEval: A Multi-Format Benchmark for Scientific Document Representations
Amanpreet Singh; Mike D?Arcy; Arman Cohan; Doug Downey; Sergey Feldman;
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Highlight: In response, we introduce SciRepEval, the first comprehensive benchmark for training and evaluating scientific document representations.
588, Boosting Summarization with Normalizing Flows and Aggressive Training
Yu Yang; Xiaotong Shen;
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Highlight: This paper presents FlowSUM, a normalizing flows-based variational encoder-decoder framework for Transformer-based summarization.
589, BioFEG: Generate Latent Features for Biomedical Entity Linking
Xuhui Sui; Ying Zhang; Xiangrui Cai; Kehui Song; Baohang Zhou; Xiaojie Yuan; Wensheng Zhang;
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Highlight: Limited by understanding these unseen entities, traditional biomedical entity linking models suffer from multiple types of linking errors. In this paper, we propose a novel latent feature generation framework BioFEG to address these challenges.
590, EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification
Yingjie Zhu; Jiasheng Si; Yibo Zhao; Haiyang Zhu; Deyu Zhou; Yulan He;
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Highlight: However, current counterfactual data augmentation techniques fail to handle multi-hop fact verification due to their incapability to preserve the complex logical relationships within multiple correlated texts. In this paper, we overcome this limitation by developing a rationale-sensitive method to generate linguistically diverse and label-flipping counterfactuals while preserving logical relationships.
591, Revisiting Source Context in Nearest Neighbor Machine Translation
Xuanhong Li; Peng Li; Po Hu;
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Highlight: However, existing research does not explicitly consider the source context when retrieving similar examples, potentially leading to suboptimal performance. To address this, we comprehensively revisit the role of source context and propose a simple and effective method for improving neural machine translation via source context enhancement, demonstrating its crucial role in both retrieving superior examples and determining more suitable interpolation coefficients.
592, A Generation-based Deductive Method for Math Word Problems
Yuxuan Hu; Jing Zhang; Haoyang Li; Cuiping Li; Hong Chen;
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Highlight: This paper propose a new multivariate directed acyclic graph (mDAG) as an alternative to the generation methods? binary expression tree or the deductive methods? binary directed acyclic graph.
593, GEM: Gestalt Enhanced Markup Language Model for Web Understanding Via Render Tree
Zirui Shao; Feiyu Gao; Zhongda Qi; Hangdi Xing; Jiajun Bu; Zhi Yu; Qi Zheng; Xiaozhong Liu;
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Highlight: This study presents an innovative Gestalt Enhanced Markup (GEM) Language Model inspired by Gestalt psychological theory for hosting heterogeneous visual information from the render tree into the language model without requiring additional visual input.
594, A Framework for Vision-Language Warm-up Tasks in Multimodal Dialogue Models
Jaewook Lee; Seongsik Park; Seong-Heum Park; Hongjin Kim; Harksoo Kim;
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Highlight: However, methods for exploiting these additional datasets can be quite limited in real-world settings, creating a need for more efficient methods for constructing agents based solely on the target dataset. To address these issues, we present a new learning strategy called vision-language warm-up tasks for multimodal dialogue models (VLAW-MDM).
595, CodeT5+: Open Code Large Language Models for Code Understanding and Generation
Yue Wang; Hung Le; Akhilesh Gotmare; Nghi Bui; Junnan Li; Steven Hoi;
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Highlight: However, existing code LLMs have two main limitations. First, they often adopt a specific architecture (encoder-only or decoder-only) or rely on a unified encoder-decoder network for different downstream tasks, lacking the flexibility to operate in the optimal architecture for a specific task. Secondly, they often employ a limited set of pretraining objectives which might not be relevant to some tasks and hence result in substantial performance degrade. To address these limitations, we propose ?CodeT5+?, a family of encoder-decoder LLMs for code in which component modules can be flexibly combined to suit a wide range of code tasks
596, How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning
Hang Chen; Xinyu Yang; Jing Luo; Wenjing Zhu;
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Highlight: To facilitate the implementation of deep learning, we introduce the cogn frameworks to handle unstructured conversation data, and employ an autoencoder architecture to regard the unobservable noise as learnable ?implicit causes.
597, BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
Qizhi Pei; Wei Zhang; Jinhua Zhu; Kehan Wu; Kaiyuan Gao; Lijun Wu; Yingce Xia; Rui Yan;
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Highlight: However, current models exhibit several limitations, such as the generation of invalid molecular SMILES, underutilization of contextual information, and equal treatment of structured and unstructured knowledge. To address these issues, we propose BioT5, a comprehensive pre-training framework that enriches cross-modal integration in biology with chemical knowledge and natural language associations.
598, Building Persona Consistent Dialogue Agents with Offline Reinforcement Learning
Ryan Shea; Zhou Yu;
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Highlight: Instead, we propose an offline RL framework to improve the persona consistency of dialogue systems.
599, Prompt-Based Monte-Carlo Tree Search for Goal-oriented Dialogue Policy Planning
Xiao Yu; Maximillian Chen; Zhou Yu;
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Highlight: We introduce GDP-Zero, an approach using Open-Loop MCTS to perform goal-oriented dialogue policy planning without any model training.
600, KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords Learning
Xiao Yu; Qingyang Wu; Kun Qian; Zhou Yu;
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Highlight: We investigate an approach to create a more efficient RL-based algorithm to improve TOD performance in an offline setting.
601, CP-BCS: Binary Code Summarization Guided By Control Flow Graph and Pseudo Code
Tong Ye; Lingfei Wu; Tengfei Ma; Xuhong Zhang; Yangkai Du; Peiyu Liu; Shouling Ji; Wenhai Wang;
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Highlight: To fully exploit the semantics of assembly code, we present a control flow graph and pseudo code guided binary code summarization framework called CP-BCS.
602, Debiasing Made State-of-the-art: Revisiting The Simple Seed-based Weak Supervision for Text Classification
Chengyu Dong; Zihan Wang; Jingbo Shang;
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Highlight: Recent advances in weakly supervised text classification mostly focus on designing sophisticated methods to turn high-level human heuristics into quality pseudo-labels. In this paper, we revisit the seed matching-based method, which is arguably the simplest way to generate pseudo-labels, and show that its power was greatly underestimated.
603, Open-world Semi-supervised Generalized Relation Discovery Aligned in A Real-world Setting
William Hogan; Jiacheng Li; Jingbo Shang;
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Highlight: Furthermore, we observe that popular relations can often be implicitly inferred through specific patterns, while long-tail relations tend to be explicitly expressed. Motivated by these insights, we present a method called KNoRD (Known and Novel Relation Discovery), which effectively classifies explicitly and implicitly expressed relations from known and novel classes within unlabeled data.
604, DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models
Xinwei Wu; Junzhuo Li; Minghui Xu; Weilong Dong; Shuangzhi Wu; Chao Bian; Deyi Xiong;
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Highlight: To effectively mitigate these risks, people often have to spend a significant amount of time and computational costs to retrain new models instead of finding ways to cure the ?sick? models. Therefore, we propose a method to locate and erase risky neurons in order to eliminate the impact of privacy data in the model.
605, Language Representation Projection: Can We Transfer Factual Knowledge Across Languages in Multilingual Language Models?
Shaoyang Xu; Junzhuo Li; Deyi Xiong;
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Highlight: This paper investigates the feasibility of explicitly transferring relatively rich factual knowledge from English to non-English languages. To accomplish this, we propose two parameter-free Language Representation Projection modules (LRP2).
606, MMNMT: Modularizing Multilingual Neural Machine Translation with Flexibly Assembled MoE and Dense Blocks
Shangjie Li; Xiangpeng Wei; Shaolin Zhu; Jun Xie; Baosong Yang; Deyi Xiong;
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Highlight: In this paper, we propose a modularized MNMT framework that is able to flexibly assemble dense and MoE-based sparse modules to achieve the best of both worlds.
607, Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models
Junpeng Li; Zixia Jia; Zilong Zheng;
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Highlight: Unfortunately, vanilla in-context learning is infeasible for DocRE due to the plenty of predefined fine-grained relation types and the uncontrolled generations of LLMs. To tackle this issue, we propose a method integrating an LLM and a natural language inference (NLI) module to generate relation triples, thereby augmenting document-level relation datasets.
608, Vision-Enhanced Semantic Entity Recognition in Document Images Via Visually-Asymmetric Consistency Learning
Hao Wang; Xiahua Chen; Rui Wang; Chenhui Chu;
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Highlight: However, existing models commonly train a visual encoder with weak cross-modal supervision signals, resulting in a limited capacity to capture these non-textual features and suboptimal performance. In this paper, we propose a novel Visually-Asymmetric coNsistenCy Learning (VANCL) approach that addresses the above limitation by enhancing the model?s ability to capture fine-grained visual and layout features through the incorporation of color priors.
609, An Expression Tree Decoding Strategy for Mathematical Equation Generation
Wenqi Zhang; Yongliang Shen; Qingpeng Nong; Zeqi Tan; Yanna Ma; Weiming Lu;
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Highlight: However, each expression represents a solving step, and there naturally exist parallel or dependent relations between these steps, which are ignored by current sequential methods. Therefore, we integrate tree structure into the expression-level generation and advocate an expression tree decoding strategy.
610, COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation
Nan Wang; Qifan Wang; Yi-Chia Wang; Maziar Sanjabi; Jingzhou Liu; Hamed Firooz; Hongning Wang; Shaoliang Nie;
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Highlight: In this work, we investigate fairness of PTG in the context of personalized explanation generation for recommendations.
611, Towards A Unified Conversational Recommendation System: Multi-task Learning Via Contextualized Knowledge Distillation
Yeongseo Jung; Eunseo Jung; Lei Chen;
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Highlight: To bridge the gap, we propose a multi-task learning for a unified CRS, where a single model jointly learns both tasks via Contextualized Knowledge Distillation (ConKD).
612, Can Language Models Understand Physical Concepts?
Lei Li; Jingjing Xu; Qingxiu Dong; Ce Zheng; Xu Sun; Lingpeng Kong; Qi Liu;
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Highlight: Analysis indicates that the rich semantics in visual representation can serve as a valuable source of embodied knowledge. Inspired by this, we propose a distillation method to transfer embodied knowledge from VLMs to LMs, achieving performance gain comparable with that by scaling up parameters of LMs 134?.
613, Relation-aware Ensemble Learning for Knowledge Graph Embedding
Ling Yue; Yongqi Zhang; Quanming Yao; Yong Li; Xian Wu; Ziheng Zhang; Zhenxi Lin; Yefeng Zheng;
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Highlight: In this paper, we propose to learn an ensemble by leveraging existing methods in a relation-aware manner.
614, Unsupervised Sounding Pixel Learning
Yining Zhang; Yanli Ji; Yang Yang;
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Highlight: In this paper, we propose an **U**nsupervised **S**ounding **P**ixel **L**earning (USPL) approach which enables a pixel-level sounding source localization in unsupervised paradigm.
615, Structure-aware Knowledge Graph-to-text Generation with Planning Selection and Similarity Distinction
Feng Zhao; Hongzhi Zou; Cheng Yan;
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Highlight: One of the primary challenges in this task is bridging the gap between the diverse structures of the KG and the target text, while preserving the details of the input KG. To address this, we propose a novel approach that efficiently integrates graph structure-aware modules with pre-trained language models.
616, Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation
Yixin Liu; Alexander Fabbri; Yilun Zhao; Pengfei Liu; Shafiq Joty; Chien-Sheng Wu; Caiming Xiong; Dragomir Radev;
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Highlight: In this work, we develop strong-performing automatic metrics for reference-based summarization evaluation, based on a two-stage evaluation pipeline that first extracts basic information units from one text sequence and then checks the extracted units in another sequence.
617, Document-level Relationship Extraction By Bidirectional Constraints of Beta Rules
Yichun Liu; Zizhong Zhu; Xiaowang Zhang; Zhiyong Feng; Daoqi Chen; Yaxin Li;
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Highlight: In this paper, we proposes Bidirectional Constraints of Beta Rules(BCBR), a novel logic constraint framework.
618, ORCHID: A Chinese Debate Corpus for Target-Independent Stance Detection and Argumentative Dialogue Summarization
Xiutian Zhao; Ke Wang; Wei Peng;
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Highlight: However, research on these tasks is limited by the insufficiency of public datasets, especially for non-English languages. To address this language resource gap in Chinese, we present ORCHID (Oral Chinese Debate), the first Chinese dataset for benchmarking target-independent stance detection and debate summarization.
619, Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future
Linyi Yang; Yaoxian Song; Xuan Ren; Chenyang Lyu; Yidong Wang; Jingming Zhuo; Lingqiao Liu; Jindong Wang; Jennifer Foster; Yue Zhang;
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Highlight: Despite these challenges, there is a lack of comprehensive surveys on the generalization challenge from an OOD perspective in natural language understanding. Therefore, this paper aims to fill this gap by presenting the first comprehensive review of recent progress, methods, and evaluations on this topic.
620, LLM-enhanced Self-training for Cross-domain Constituency Parsing
Jianling Li; Meishan Zhang; Peiming Guo; Min Zhang; Yue Zhang;
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Highlight: Self-training has proven to be an effective approach for cross-domain tasks, and in this study, we explore its application to cross-domain constituency parsing.
621, Predict The Future from The Past? On The Temporal Data Distribution Shift in Financial Sentiment Classifications
Yue Guo; Chenxi Hu; Yi Yang;
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Highlight: In this paper, we conduct an empirical study on the financial sentiment analysis system under temporal data distribution shifts using a real-world financial social media dataset that spans three years.
622, Fast and Accurate Factual Inconsistency Detection Over Long Documents
Barrett Lattimer; Patrick CHen; Xinyuan Zhang; Yi Yang;
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Highlight: We introduce SCALE (Source Chunking Approach for Large-scale inconsistency Evaluation), a task-agnostic model for detecting factual inconsistencies using a novel chunking strategy.
623, Let?s Think Frame By Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought
Vaishnavi Himakunthala; Andy Ouyang; Daniel Rose; Ryan He; Alex Mei; Yujie Lu; Chinmay Sonar; Michael Saxon; William Wang;
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Highlight: Inspired by visually descriptive scene plays, we propose two formats for keyframe description: unstructured dense captions and structured scene descriptions that identify the focus, action, mood, objects, and setting (FAMOuS) of the keyframe.
624, Parameter-efficient Tuning for Large Language Model Without Calculating Its Gradients
Feihu Jin; Jiajun Zhang; Chengqing Zong;
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Highlight: This paper proposes a novel parameter-efficient tuning method for LLMs without calculating their gradients.
625, Structural Generalization in COGS: Supertagging Is (almost) All You Need
Alban Petit; Caio Corro; Fran?ois Yvon;
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Highlight: In particular, several recent semantic parsing datasets have put forward important limitations of neural networks in cases where compositional generalization is required. In this work, we extend a neural graph-based parsing framework in several ways to alleviate this issue, notably: (1) the introduction of a supertagging step with valency constraints, expressed as an integer linear program; (2) the reduction of the graph prediction problem to the maximum matching problem; (3) the design of an incremental early-stopping training strategy to prevent overfitting.
626, Self-Influence Guided Data Reweighting for Language Model Pre-training
Megh Thakkar; Tolga Bolukbasi; Sriram Ganapathy; Shikhar Vashishth; Sarath Chandar; Partha Talukdar;
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Highlight: While data reweighting has been explored in the context of task-specific supervised learning and LM fine-tuning, model-driven reweighting for pretraining data has not been explored. We fill this important gap and propose PRESENCE, a method for jointly reweighting samples by leveraging self-influence (SI) scores as an indicator of sample importance and pre-training.
627, NAIL: Lexical Retrieval Indices with Efficient Non-Autoregressive Decoders
Livio Soares; Daniel Gillick; Jeremy Cole; Tom Kwiatkowski;
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Highlight: However, the best models require dedicated hardware for serving, which is costly and often not feasible. To avoid this servingtime requirement, we present a method of capturing up to 86% of the gains of a Transformer cross-attention model with a lexicalized scoring function that only requires 10-6% of the Transformer?s FLOPs per document and can be served using commodity CPUs.
628, FinGPT: Large Generative Models for A Small Language
Risto Luukkonen; Ville Komulainen; Jouni Luoma; Anni Eskelinen; Jenna Kanerva; Hanna-Mari Kupari; Filip Ginter; Veronika Laippala; Niklas Muennighoff; Aleksandra Piktus; Thomas Wang; Nouamane Tazi; Teven Scao; Thomas Wolf; Osma Suominen; Samuli Sairanen; Mikko Merioksa; Jyrki Heinonen; Aija Vahtola; Samuel Antao; Sampo Pyysalo;
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Highlight: In this work, we study the challenges of creating LLMs for Finnish, a language spoken by less than 0.
629, S2abEL: A Dataset for Entity Linking from Scientific Tables
Yuze Lou; Bailey Kuehl; Erin Bransom; Sergey Feldman; Aakanksha Naik; Doug Downey;
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Highlight: We introduce a neural baseline method designed for EL on scientific tables containing many out-of-knowledge-base mentions, and show that it significantly outperforms a state-of-the-art generic table EL method.
630, DeSIQ: Towards An Unbiased, Challenging Benchmark for Social Intelligence Understanding
Xiao-Yu Guo; Yuan-Fang Li; Reza Haf;
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Highlight: We introduce DeSIQ, a new challenging dataset, constructed by applying simple perturbations to Social-IQ.
631, Enhancing Low-resource Fine-grained Named Entity Recognition By Leveraging Coarse-grained Datasets
Su Lee; Seokjin Oh; Woohwan Jung;
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Highlight: We propose a fine-grained NER model with a Fine-to-Coarse(F2C) mapping matrix to leverage the hierarchical structure explicitly.
632, SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation
Junfeng Jiang; Chengzhang Dong; Sadao Kurohashi; Akiko Aizawa;
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Highlight: In this paper, we provide a feasible definition of dialogue segmentation points with the help of document-grounded dialogues and release a large-scale supervised dataset called SuperDialseg, containing 9,478 dialogues based on two prevalent document-grounded dialogue corpora, and also inherit their useful dialogue-related annotations.
633, Human Learning By Model Feedback: The Dynamics of Iterative Prompting with Midjourney
Shachar Don-Yehiya; Leshem Choshen; Omri Abend;
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Highlight: Generating images with a Text-to-Image model often requires multiple trials, where human users iteratively update their prompt based on feedback, namely the output image. Taking inspiration from cognitive work on reference games and dialogue alignment, this paper analyzes the dynamics of the user prompts along such iterations.
634, Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents
Hyungjoo Chae; Yongho Song; Kai Ong; Taeyoon Kwon; Minjin Kim; Youngjae Yu; Dongha Lee; Dongyeop Kang; Jinyoung Yeo;
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Highlight: To this end, we propose a knowledge distillation framework that leverages LLMs as unreliable teachers and selectively distills consistent and helpful rationales via alignment filters.
635, ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing Using Large Language Models
Dheeraj Mekala; Jason Wolfe; Subhro Roy;
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Highlight: In this work, we propose ZEROTOP, a zero-shot task-oriented parsing method that decomposes semantic parsing problem into a set of abstractive and extractive question-answering (QA) problems.
636, MAF: Multi-Aspect Feedback for Improving Reasoning in Large Language Models
Deepak Nathani; David Wang; Liangming Pan; William Wang;
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Highlight: In this work, we propose **Multi-Aspect Feedback**, an iterative refinement framework that integrates multiple feedback modules, including frozen LMs and external tools, each focusing on a specific error category.
637, Event Causality Extraction Via Implicit Cause-Effect Interactions
Jintao Liu; Zequn Zhang; Kaiwen Wei; Zhi Guo; Xian Sun; Li Jin; Xiaoyu Li;
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Highlight: To this end, we propose an Implicit Cause-Effect interaction (ICE) framework, which formulates ECE as a template-based conditional generation problem.
638, Causal Document-Grounded Dialogue Pre-training
Yingxiu Zhao; Bowen Yu; Bowen Li; Haiyang Yu; Jinyang Li; Chao Wang; Fei Huang; Yongbin Li; Nevin Zhang;
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Highlight: While task-specific pre-training has significantly enhanced performances on numerous downstream tasks, existing DocGD methods still rely on general pre-trained language models without a specifically tailored pre-training approach that explicitly captures the causal relationships. To address this, we present the first causally-complete dataset construction strategy for developing million-scale DocGD pre-training corpora.
639, JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification
Henry Zou; Cornelia Caragea;
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Highlight: However, existing approaches based on pseudo-labeling suffer from the issues of pseudo-label bias and error accumulation. In this paper, we propose JointMatch, a holistic approach for SSTC that addresses these challenges by unifying ideas from recent semi-supervised learning and the task of learning with noise.
640, Copyright Violations and Large Language Models
Antonia Karamolegkou; Jiaang Li; Li Zhou; Anders S?gaard;
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Highlight: This work explores the issue of copyright violations and large language models through the lens of verbatim memorization, focusing on possible redistribution of copyrighted text. We present experiments with a range of language models over a collection of popular books and coding problems, providing a conservative characterization of the extent to which language models can redistribute these materials.
641, Language Model Quality Correlates with Psychometric Predictive Power in Multiple Languages
Ethan Wilcox; Clara Meister; Ryan Cotterell; Tiago Pimentel;
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Highlight: In this work, we conduct a systematic crosslinguistic assessment of the QP hypothesis.
642, SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples
Deqing Fu; Ameya Godbole; Robin Jia;
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Highlight: In this work, we propose Self-labeled Counterfactuals for Extrapolating to Negative Examples (SCENE), an automatic method for synthesizing training data that greatly improves models? ability to detect challenging negative examples.
643, Superlim: A Swedish Language Understanding Evaluation Benchmark
Aleksandrs Berdicevskis; Gerlof Bouma; Robin Kurtz; Felix Morger; Joey ?hman; Yvonne Adesam; Lars Borin; Dana Dann?lls; Markus Forsberg; Tim Isbister; Anna Lindahl; Martin Malmsten; Faton Rekathati; Magnus Sahlgren; Elena Volodina; Love B?rjeson; Simon Hengchen; Nina Tahmasebi;
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Highlight: We present Superlim, a multi-task NLP benchmark and analysis platform for evaluating Swedish language models, a counterpart to the English-language (Super)GLUE suite.
644, IDTraffickers: An Authorship Attribution Dataset to Link and Connect Potential Human-Trafficking Operations on Text Escort Advertisements
Vageesh Saxena; Benjamin Ashpole; Gijs van Dijck; Gerasimos Spanakis;
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Highlight: Consequently, identifying and connecting HT vendors has become increasingly challenging for Law Enforcement Agencies (LEAs). To address this issue, we introduce IDTraffickers, an extensive dataset consisting of 87,595 text ads and 5,244 vendor labels to enable the verification and identification of potential HT vendors on online escort markets.
645, TrlX: A Framework for Large Scale Reinforcement Learning from Human Feedback
Alexander Havrilla; Maksym Zhuravinskyi; Duy Phung; Aman Tiwari; Jonathan Tow; Stella Biderman; Quentin Anthony; Louis Castricato;
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Highlight: Current RLHF paradigms rely on Proximal Policy Optimization (PPO), which quickly becomes a challenge to implement and scale up to large architectures. To address this difficulty we present the AutoRLHF library as a feature complete open-source framework for RLHF fine-tuning of models up to and exceeding 70 billion parameters.
646, Regulation and NLP (RegNLP): Taming Large Language Models
Catalina Goanta; Nikolaos Aletras; Ilias Chalkidis; Sofia Ranchord?s; Gerasimos Spanakis;
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Highlight: This resource has largely remained untapped so far. In this paper, we argue how NLP research on these topics can benefit from proximity to regulatory studies and adjacent fields.
647, Detecting and Mitigating Hallucinations in Multilingual Summarisation
Yifu Qiu; Yftah Ziser; Anna Korhonen; Edoardo Ponti; Shay Cohen;
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Highlight: With the existing faithful metrics focusing on English, even measuring the extent of this phenomenon in cross-lingual settings is hard. To address this, we first develop a novel metric, mFACT, evaluating the faithfulness of non-English summaries, leveraging translation-based transfer from multiple English faithfulness metrics.
648, IC3: Image Captioning By Committee Consensus
David Chan; Austin Myers; Sudheendra Vijayanarasimhan; David Ross; John Canny;
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Highlight: In this work, we introduce a simple, yet novel, method: ?Image Captioning by Committee Consensus? (IC3), designed to generate a single caption that captures high-level details from several annotator viewpoints.
649, Understanding Computational Models of Semantic Change: New Insights from The Speech Community
Filip Miletic; Anne Przewozny-Desriaux; Ludovic Tanguy;
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Highlight: We focus on the sociolinguistic issue of contact-induced semantic shifts in Quebec English, and analyze 40 target words using type-level and token-level word embeddings, empirical linguistic properties, and ? crucially ? acceptability ratings and qualitative remarks by 15 speakers from Montreal.
650, On The Benefits of Learning to Route in Mixture-of-Experts Models
Nishanth Dikkala; Nikhil Ghosh; Raghu Meka; Rina Panigrahy; Nikhil Vyas; Xin Wang;
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Highlight: We show theoretical and empirical evidence that the router?s ability to route tokens intelligently confers a significant advantage to MoE models.
651, DIVE: Towards Descriptive and Diverse Visual Commonsense Generation
Jun-Hyung Park; Hyuntae Park; Youjin Kang; Eojin Jeon; SangKeun Lee;
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Highlight: In this work, we propose a novel visual commonsense generation framework, called DIVE, which aims to improve the descriptiveness and diversity of generated inferences.
652, A Video Is Worth 4096 Tokens: Verbalize Story Videos To Understand Them In Zero Shot
Aanisha Bhattacharyya; Yaman Singla; Balaji Krishnamurthy; Rajiv Shah; Changyou Chen;
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Highlight: On the other hand, the rise of large language models (LLMs) has witnessed remarkable zero-shot performance in various natural language processing (NLP) tasks, such as emotion classification, question-answering, and topic classification. To leverage such advanced techniques to bridge this performance gap in multimedia understanding, we propose verbalizing long videos to generate their descriptions in natural language, followed by performing video-understanding tasks on the generated story as opposed to the original video.
653, Prompting Scientific Names for Zero-Shot Species Recognition
Shubham Parashar; Zhiqiu Lin; Yanan Li; Shu Kong;
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Highlight: This is because these names are usually not included in CLIP?s training set. To improve performance, we explore using large-language models (LLMs) to generate descriptions (e. g. , of species color and shape) and additionally use them in prompts.
654, The ACL OCL Corpus: Advancing Open Science in Computational Linguistics
Shaurya Rohatgi; Yanxia Qin; Benjamin Aw; Niranjana Unnithan; Min-Yen Kan;
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Highlight: We present ACL OCL, a scholarly corpus derived from the ACL Anthology to assist Open scientific research in the Computational Linguistics domain.
655, People Make Better Edits: Measuring The Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection
Indira Sen; Dennis Assenmacher; Mattia Samory; Isabelle Augenstein; Wil Aalst; Claudia Wagner;
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Highlight: However, manually generating CADs can be time-consuming and expensive. Hence in this work, we assess if this task can be automated using generative NLP models.
656, Interventional Rationalization
Linan Yue; Qi Liu; Li Wang; Yanqing An; Yichao Du; Zhenya Huang;
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Highlight: Inspired by the causal theory, in this paper, we develop an interventional rationalization (Inter-RAT) to discover the causal rationales.
657, DiffS2UT: A Semantic Preserving Diffusion Model for Textless Direct Speech-to-Speech Translation
Yongxin Zhu; Zhujin Gao; Xinyuan Zhou; Ye Zhongyi; Linli Xu;
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Highlight: In this paper, we propose a novel diffusion model by applying the diffusion forward process in the continuous speech representation space, while employing the diffusion backward process in the discrete speech unit space.
658, Back Transcription As A Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors
Marek Kubis; Pawel Sk?rzewski; Marcin Sowannski; Tomasz Zietkiewicz;
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Highlight: This paper proposes a method for investigating the impact of speech recognition errors on the performance of natural language understanding models.
659, Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models
Geewook Kim; Hodong Lee; Daehee Kim; Haeji Jung; Sanghee Park; Yoonsik Kim; Sangdoo Yun; Taeho Kil; Bado Lee; Seunghyun Park;
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Highlight: In this paper, we introduce Contrastive Reading Model (Cream), a novel neural architecture designed to enhance the language-image understanding capability of LLMs by capturing intricate details that are often overlooked in existing methods.
660, ?Are Your Explanations Reliable?? Investigating The Stability of LIME in Explaining Text Classifiers By Marrying XAI and Adversarial Attack
Christopher Burger; Lingwei Chen; Thai Le;
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Highlight: However, its stability remains little explored, especially in the context of text data, due to the unique text-space constraints. To address these challenges, in this paper, we first evaluate the inherent instability of LIME on text data to establish a baseline, and then propose a novel algorithm XAIFooler to perturb text inputs and manipulate explanations that casts investigation on the stability of LIME as a text perturbation optimization problem.
661, Multi-Task Knowledge Distillation with Embedding Constraints for Scholarly Keyphrase Boundary Classification
Seo Park; Cornelia Caragea;
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Highlight: In this work, we propose a novel embedding constraint on multi-task knowledge distillation which enforces the teachers (single-task models) and the student (multi-task model) similarity in the embedding space.
662, CLAIR: Evaluating Image Captions with Large Language Models
David Chan; Suzanne Petryk; Joseph Gonzalez; Trevor Darrell; John Canny;
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Highlight: Here, we propose CLAIR, a novel method that leverages the zero-shot language modeling capabilities of large language models (LLMs) to evaluate candidate captions.
663, CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQL
Mayank Kothyari; Dhruva Dhingra; Sunita Sarawagi; Soumen Chakrabarti;
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Highlight: In response, we propose a two-stage process for effective coverage during retrieval.
664, ChatEdit: Towards Multi-turn Interactive Facial Image Editing Via Dialogue
Xing Cui; Zekun Li; Pei Li; Yibo Hu; Hailin Shi; Chunshui Cao; Zhaofeng He;
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Highlight: The dataset is challenging, as it requires the system to dynamically track and edit images based on user requests, while generating appropriate natural language responses. To address these challenges, we propose a framework comprising a dialogue module for tracking user requests as well as generating responses, and an image editing module for editing images accordingly.
665, Challenges in Context-Aware Neural Machine Translation
Linghao Jin; Jacqueline He; Jonathan May; Xuezhe Ma;
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Highlight: In this work, we investigate and present several core challenges that impede progress within the field, relating to discourse phenomena, context usage, model architectures, and document-level evaluation.
666, TIMELINE: Exhaustive Annotation of Temporal Relations Supporting The Automatic Ordering of Events in News Articles
Sarah Alsayyahi; Riza Batista-Navarro;
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Highlight: Temporal relation extraction models have thus far been hindered by a number of issues in existing temporal relation-annotated news datasets, including: (1) low inter-annotator agreement due to the lack of specificity of their annotation guidelines in terms of what counts as a temporal relation; (2) the exclusion of long-distance relations within a given document (those spanning across different paragraphs); and (3) the exclusion of events that are not centred on verbs. This paper aims to alleviate these issues by presenting a new annotation scheme that clearly defines the criteria based on which temporal relations should be annotated.
667, FAME: Flexible, Scalable Analogy Mappings Engine
Shahar Jacob; Chen Shani; Dafna Shahaf;
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Highlight: In this work, we relax the input requirements, requiring only names of entities to be mapped.
668, Interpreting and Exploiting Functional Specialization in Multi-Head Attention Under Multi-task Learning
Chong Li; Shaonan Wang; Yunhao Zhang; Jiajun Zhang; Chengqing Zong;
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Highlight: If it is, can this mechanism further improve the model performance? To investigate these questions, we introduce an interpreting method to quantify the degree of functional specialization in multi-head attention.
669, AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications
Bhaktipriya Radharapu; Kevin Robinson; Lora Aroyo; Preethi Lahoti;
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Highlight: We introduce an AI-assisted approach for automated generation of adversarial evaluation datasets to test the safety of LLM generations on new downstream applications.
670, EELBERT: Tiny Models Through Dynamic Embeddings
Gabrielle Cohn; Rishika Agarwal; Deepanshu Gupta; Siddharth Patwardhan;
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Highlight: We introduce EELBERT, an approach for compression of transformer-based models (e. g. , BERT), with minimal impact on the accuracy of downstream tasks.
671, ViGPTQA - State-of-the-Art LLMs for Vietnamese Question Answering: System Overview, Core Models Training, and Evaluations
Minh Thuan Nguyen; Khanh Tung Tran; Nhu Van Nguyen; Xuan-Son Vu;
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Highlight: This paper introduces a practical real-world implementation of a question answering system for Vietnamese, called ViGPTQA, leveraging the power of LLM.
672, Absolute Position Embedding Learns Sinusoid-like Waves for Attention Based on Relative Position
Yuji Yamamoto; Takuya Matsuzaki;
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Highlight: We analyze the mechanism behind the concentration of attention on nearby tokens. We show that the phenomenon emerges as follows: (1) learned position embedding has sinusoid-like components, (2) such components are transmitted to the query and the key in the self-attention, (3) the attention head shifts the phases of the sinusoid-like components so that the attention concentrates on nearby tokens at specific relative positions.
673, Fine-grained Conversational Decoding Via Isotropic and Proximal Search
Yuxuan Yao; Han Wu; Qiling Xu; Linqi Song;
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Highlight: Inspired by SimDRC that a good dialogue feature space should follow the rules of locality and isotropy, we present a fine-grained conversational decoding method, termed isotropic and proximal search (IPS).
674, Holistic Inter-Annotator Agreement and Corpus Coherence Estimation in A Large-scale Multilingual Annotation Campaign
Nicolas Stefanovitch; Jakub Piskorski;
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Highlight: In this paper we report on the complexity of persuasion technique annotation in the context of a large multilingual annotation campaign involving 6 languages and approximately 40 annotators.
675, Evaluating The Rationale Understanding of Critical Reasoning in Logical Reading Comprehension
Akira Kawabata; Saku Sugawara;
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Highlight: To precisely evaluate a language model?s capability for logical reading comprehension, we present a dataset for testing the understanding of the rationale behind critical reasoning.
676, Theory of Mind for Multi-Agent Collaboration Via Large Language Models
Huao Li; Yu Chong; Simon Stepputtis; Joseph Campbell; Dana Hughes; Charles Lewis; Katia Sycara;
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Highlight: This study evaluates LLM-based agents in a multi-agent cooperative text game with Theory of Mind (ToM) inference tasks, comparing their performance with Multi-Agent Reinforcement Learning (MARL) and planning-based baselines.
677, GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP
Md Tawkat Islam Khondaker; Abdul Waheed; El Moatez Billah Nagoudi; Muhammad Abdul-Mageed;
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Highlight: However, the model?s efficacy across diverse linguistic contexts remains largely uncharted territory. This work aims to bridge this knowledge gap, with a primary focus on assessing ChatGPT?s capabilities on Arabic languages and dialectal varieties.
678, Cultural Concept Adaptation on Multimodal Reasoning
Zhi Li; Yin Zhang;
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Highlight: This is largely due to the difficulty of data scarcity and expensive annotation. In this paper, we navigate this uncharted territory by leveraging high-resource cultures to facilitate comprehension of low-resource ones.
679, Event Ontology Completion with Hierarchical Structure Evolution Networks
Pengfei Cao; Yupu Hao; Yubo Chen; Kang Liu; Jiexin Xu; Huaijun Li; Xiaojian Jiang; Jun Zhao;
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Highlight: In this paper, we propose a new research task named Event Ontology Completion (EOC), which aims to simultaneously achieve event clustering, hierarchy expansion and type naming.
680, GATITOS: Using A New Multilingual Lexicon for Low-resource Machine Translation
Alexander Jones; Isaac Caswell; Orhan Firat; Ishank Saxena;
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Highlight: We test the efficacy of bilingual lexica in a real-world set-up, on 200-language translation models trained on web-crawled text. We present several findings: (1) using lexical data augmentation, we demonstrate sizable performance gains for unsupervised translation; (2) we compare several families of data augmentation, demonstrating that they yield similar improvements, and can be combined for even greater improvements; (3) we demonstrate the importance of carefully curated lexica over larger, noisier ones, especially with larger models; and (4) we compare the efficacy of multilingual lexicon data versus human-translated parallel data.
681, Using Interpretation Methods for Model Enhancement
Zhuo Chen; Chengyue Jiang; Kewei Tu;
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Highlight: In this paper, we propose a framework of utilizing interpretation methods and gold rationales to enhance models.
682, Diversity Enhanced Narrative Question Generation for Storybooks
Hokeun Yoon; JinYeong Bak;
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Highlight: In this paper, we introduce a multi-question generation model (mQG), which is capable of generating multiple, diverse, and answerable questions by focusing on context and questions.
683, Compressing and Debiasing Vision-Language Pre-Trained Models for Visual Question Answering
Qingyi Si; Yuanxin Liu; Zheng Lin; Peng Fu; Yanan Cao; Weiping Wang;
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Highlight: This paper investigates whether a VLP can be compressed and debiased simultaneously by searching sparse and robust subnetworks.
684, Selectively Answering Ambiguous Questions
Jeremy Cole; Michael Zhang; Daniel Gillick; Julian Eisenschlos; Bhuwan Dhingra; Jacob Eisenstein;
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Highlight: We investigate question answering from this perspective, focusing on answering a subset of questions with a high degree of accuracy, from a set of questions in which many are inherently ambiguous.
685, Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning
Dong-Ho Lee; Kian Ahrabian; Woojeong Jin; Fred Morstatter; Jay Pujara;
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Highlight: In this paper, we develop an approach to use in-context learning (ICL) with large language models (LLMs) for TKG forecasting.
686, Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models
Yiyuan Li; Rakesh Menon; Sayan Ghosh; Shashank Srivastava;
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Highlight: However, it remains unclear if recent foundation models (Bommasani et al. , 2021) possess this ability due to the absence of direct training signals. To explore this, we introduce QuRe, a crowd-sourced dataset of human-annotated generalized quantifiers in Wikipedia sentences featuring percentage-equipped predicates.
687, Gradient-based Gradual Pruning for Language-Specific Multilingual Neural Machine Translation
Dan He; Minh-Quang Pham; Thanh-Le Ha; Marco Turchi;
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Highlight: This degradation is commonly attributed to parameter interference, which occurs when parameters are fully shared across all language pairs. In this work, to tackle this issue we propose a gradient-based gradual pruning technique for MNMT.
688, Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition
Chenxu Wang; Ping Jian; Mu Huang;
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Highlight: Fortunately, there is a wealth of unannotated utterances with explicit connectives, that can be utilized to acquire enriched discourse relation features. In light of such motivation, we propose a Prompt-based Logical Semantics Enhancement (PLSE) method for IDRR.
689, Conceptual Structure Coheres in Human Cognition But Not in Large Language Models
Siddharth Suresh; Kushin Mukherjee; Xizheng Yu; Wei-Chun Huang; Lisa Padua; Timothy Rogers;
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Highlight: The current work uses three common techniques borrowed from cognitive psychology to estimate and compare lexical-semantic structure in both humans and a well-known large language model, the DaVinci variant of GPT-3.
690, Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis
Haoyu Zhang; Yu Wang; Guanghao Yin; Kejun Liu; Yuanyuan Liu; Tianshu Yu;
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Highlight: Though Multimodal Sentiment Analysis (MSA) proves effective by utilizing rich information from multiple sources (*e. g. ,* language, video, and audio), the potential sentiment-irrelevant and conflicting information across modalities may hinder the performance from being further improved. To alleviate this, we present Adaptive Language-guided Multimodal Transformer (ALMT), which incorporates an Adaptive Hyper-modality Learning (AHL) module to learn an irrelevance/conflict-suppressing representation from visual and audio features under the guidance of language features at different scales.
691, Multitask Multimodal Prompted Training for Interactive Embodied Task Completion
Georgios Pantazopoulos; Malvina Nikandrou; Amit Parekh; Bhathiya Hemanthage; Arash Eshghi; Ioannis Konstas; Verena Rieser; Oliver Lemon; Alessandro Suglia;
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Highlight: Interactive and embodied tasks pose at least two fundamental challenges to existing Vision & Language (VL) models, including 1) grounding language in trajectories of actions and observations, and 2) referential disambiguation. To tackle these challenges, we propose an Embodied MultiModal Agent (EMMA): a unified encoder-decoder model that reasons over images and trajectories, and casts action prediction as multimodal text generation.
692, Analyzing Norm Violations in Live-Stream Chat
Jihyung Moon; Dong-Ho Lee; Hyundong Cho; Woojeong Jin; Chan Park; Minwoo Kim; Jonathan May; Jay Pujara; Sungjoon Park;
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Highlight: In this work, we share the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms.
693, Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus
Tianhang Zhang; Lin Qiu; Qipeng Guo; Cheng Deng; Yue Zhang; Zheng Zhang; Chenghu Zhou; Xinbing Wang; Luoyi Fu;
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Highlight: In this paper, we propose a novel reference-free, uncertainty-based method for detecting hallucinations in LLMs.
694, Symbol Tuning Improves In-context Learning in Language Models
Jerry Wei; Le Hou; Andrew Lampinen; Xiangning Chen; Da Huang; Yi Tay; Xinyun Chen; Yifeng Lu; Denny Zhou; Tengyu Ma; Quoc Le;
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Highlight: We present symbol tuning - finetuning language models on in-context input-label pairs where natural language labels (e. g. , ?positive/negative sentiment?) are replaced with arbitrary symbols (e. g. , ?foo/bar?).
695, The Neural Dynamics of Word Recognition and Integration
Jon Gauthier; Roger Levy;
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Highlight: Listeners recognize and integrate words in rapid and noisy everyday speech by combining expectations about upcoming content with incremental sensory evidence. We present a computational model of word recognition which formalizes this perceptual process in Bayesian decision theory.
696, Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models
Gangwoo Kim; Sungdong Kim; Byeongguk Jeon; Joonsuk Park; Jaewoo Kang;
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Highlight: To cope with the challenge, we propose a novel framework, Tree of Clarifications (ToC): It recursively constructs a tree of disambiguations for the AQ?via few-shot prompting leveraging external knowledge?and uses it to generate a long-form answer.
697, Incorporating Worker Perspectives Into MTurk Annotation Practices for NLP
Olivia Huang; Eve Fleisig; Dan Klein;
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Highlight: We found that worker preferences are often at odds with received wisdom among NLP researchers.
698, Look-back Decoding for Open-Ended Text Generation
Nan Xu; Chunting Zhou; Asli Celikyilmaz; Xuezhe Ma;
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Highlight: In this paper, we propose Look-back, an improved decoding algorithm that leverages the Kullback?Leibler divergence to track the distribution distance between current and historical decoding steps.
699, Hyperpolyglot LLMs: Cross-Lingual Interpretability in Token Embeddings
Andrea Wen-Yi; David Mimno;
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Highlight: This ubiquitous layer of language models is often overlooked. We find that similarities between these input embeddings are highly interpretable and that the geometry of these embeddings differs between model families.
700, ?Don?t Get Too Technical with Me?: A Discourse Structure-Based Framework for Automatic Science Journalism
Ronald Cardenas; Bingsheng Yao; Dakuo Wang; Yufang Hou;
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Highlight: Science journalism refers to the task of reporting technical findings of a scientific paper as a less technical news article to the general public audience. We aim to design an automated system to support this real-world task (i. e. , automatic science journalism ) by 1) introducing a newly-constructed and real-world dataset (SciTechNews), with tuples of a publicly-available scientific paper, its corresponding news article, and an expert-written short summary snippet; 2) proposing a novel technical framework that integrates a paper?s discourse structure with its metadata to guide generation; and, 3) demonstrating with extensive automatic and human experiments that our model outperforms other baseline methods (e. g. Alpaca and ChatGPT) in elaborating a content plan meaningful for the target audience, simplify the information selected, and produce a coherent final report in a layman?s style.
701, Penalty Decoding: Well Suppress The Self-Reinforcement Effect in Open-Ended Text Generation
Wenhong Zhu; Hongkun Hao; Rui Wang;
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Highlight: However, determining the optimal repetition penalty value is challenging. To tackle this, we propose a forgetting mechanism that disregards distant tokens, reducing the burden of penalty selection.
702, Clinical Contradiction Detection
Dave Makhervaks; Plia Gillis; Kira Radinsky;
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Highlight: We present a distant supervision approach that leverages a medical ontology to build a seed of potential clinical contradictions over 22 million medical abstracts.
703, Text-Transport: Toward Learning Causal Effects of Natural Language
Victoria Lin; Louis-Philippe Morency; Eli Ben-Michael;
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Highlight: In this paper, we introduce Text-Transport, a method for estimation of causal effects from natural language under any text distribution.
704, Length Is A Curse and A Blessing for Document-level Semantics
Chenghao Xiao; Yizhi Li; G Hudson; Chenghua Lin; Noura Al Moubayed;
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Highlight: In this work, we question the length generalizability of CL-based models, i. e. , their vulnerability towards length-induced semantic shift.
705, WSDMS: Debunk Fake News Via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom
Ruichao Yang; Wei Gao; Jing Ma; Hongzhan Lin; Zhiwei Yang;
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Highlight: In this research, we investigate a novel task in the field of fake news debunking, which involves detecting sentence-level misinformation.
706, Diversify Question Generation with Retrieval-Augmented Style Transfer
Qi Gou; Zehua Xia; Bowen Yu; Haiyang Yu; Fei Huang; Yongbin Li; Nguyen Cam-Tu;
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Highlight: These methods, however, have not considered the potential of external knowledge for expression diversity. To bridge this gap, we propose RAST, a framework for Retrieval-Augmented Style Transfer, where the objective is to utilize the style of diverse templates for question generation.
707, Interpreting Embedding Spaces By Conceptualization
Adi Simhi; Shaul Markovitch;
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Highlight: In this paper, we present a novel method of understanding embeddings by transforming a latent embedding space into a comprehensible conceptual space.
708, Towards Example-Based NMT with Multi-Levenshtein Transformers
Maxime Bouthors; Josep Crego; Fran?ois Yvon;
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Highlight: This is because RAMT not only improves translation metrics, but is also assumed to implement some form of domain adaptation. In this contribution, we study another salient trait of RAMT, its ability to make translation decisions more transparent by allowing users to go back to examples that contributed to these decisions.
709, DUnE: Dataset for Unified Editing
Afra Aky?rek; Eric Pan; Garry Kuwanto; Derry Wijaya;
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Highlight: In this study, we broaden the scope of the editing problem to include an array of editing cases such as debiasing and rectifying reasoning errors and define an edit as any natural language expression that solicits a change in the model?s outputs.
710, A Suite of Generative Tasks for Multi-Level Multimodal Webpage Understanding
Andrea Burns; Krishna Srinivasan; Joshua Ainslie; Geoff Brown; Bryan Plummer; Kate Saenko; Jianmo Ni; Mandy Guo;
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Highlight: To study multimodal webpage understanding, we introduce the Wikipedia Webpage suite (WikiWeb2M) containing 2M pages with all of the associated image, text, and structure data.
711, Democratizing Reasoning Ability: Tailored Learning from Large Language Model
Zhaoyang Wang; Shaohan Huang; Yuxuan Liu; Jiahai Wang; Minghui Song; Zihan Zhang; Haizhen Huang; Furu Wei; Weiwei Deng; Feng Sun; Qi Zhang;
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Highlight: However, the reasoning ability which is more challenging to foster, is relatively rarely explored. In this paper, we propose a tailored learning approach to distill such reasoning ability to smaller LMs to facilitate the democratization of the exclusive reasoning ability.
712, OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization
Shmuel Amar; Liat Schiff; Ori Ernst; Asi Shefer; Ori Shapira; Ido Dagan;
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Highlight: To advance research on more realistic scenarios, we introduce OpenAsp, a benchmark for multi-document open aspect-based summarization.
713, PEFTDebias : Capturing Debiasing Information Using PEFTs
Sumit Agarwal; Aditya Veerubhotla; Srijan Bansal;
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Highlight: In this paper, we introduce PEFTDebias, a novel approach that employs parameter-efficient fine-tuning (PEFT) to mitigate the biases within foundation models.
714, Combining Denoising Autoencoders with Contrastive Learning to Fine-tune Transformer Models
Alejo Lopez-Avila; V?ctor Su?rez-Paniagua;
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Highlight: In this work, we propose a 3-Phase technique to adjust a base model for a classification task.
715, VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining
Ramon Ruiz-Dolz; Javier Sanchez;
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Highlight: In this paper, we describe VivesDebate-Speech, a corpus of spoken argumentation created to leverage audio features for argument mining tasks.
716, Tagging-Assisted Generation Model with Encoder and Decoder Supervision for Aspect Sentiment Triplet Extraction
Luo Xianlong; Meng Yang; Yihao Wang;
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Highlight: These limitations can hinder the extraction of implicit aspects and opinions. To address these challenges, we propose a tagging-assisted generation model with encoder and decoder supervision (TAGS), which enhances the supervision of the encoder and decoder through multiple-perspective tagging assistance and label semantic representations.
717, Norm of Word Embedding Encodes Information Gain
Momose Oyama; Sho Yokoi; Hidetoshi Shimodaira;
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Highlight: Focusing on the skip-gram with negative-sampling method, we found that the squared norm of static word embedding encodes the information gain conveyed by the word; the information gain is defined by the Kullback-Leibler divergence of the co-occurrence distribution of the word to the unigram distribution.
718, Promoting Topic Coherence and Inter-Document Consorts in Multi-Document Summarization Via Simplicial Complex and Sheaf Graph
Yash Atri; Arun Iyer; Tanmoy Chakraborty; Vikram Goyal;
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Highlight: These limitations impel the systems to produce summaries that are non-factual and unfaithful, thereby imparting an unfair comprehension of the topic to the readers. To counter these limitations and promote the information equivalence between the source document and generated summary, we propose FIBER, a novel encoder-decoder model that uses pre-trained BART to comprehensively analyze linguistic nuances, simplicial complex layer to apprehend inherent properties that transcend pairwise associations and sheaf graph attention to effectively capture the heterophilic properties.
719, MAGNIFICo: Evaluating The In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations
Arkil Patel; Satwik Bhattamishra; Siva Reddy; Dzmitry Bahdanau;
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Highlight: In this paper, we systematically analyse the ability of LLMs to acquire novel interpretations using in-context learning.
720, Generating and Evaluating Tests for K-12 Students with Language Model Simulations: A Case Study on Sentence Reading Efficiency
Eric Zelikman; Wanjing Ma; Jasmine Tran; Diyi Yang; Jason Yeatman; Nick Haber;
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Highlight: In this study, we focus on tests of silent sentence reading efficiency, used to assess students? reading ability over time.
721, MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition
Shuhui Wu; Yongliang Shen; Zeqi Tan; Wenqi Ren; Jietian Guo; Shiliang Pu; Weiming Lu;
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Highlight: In this paper, we propose a noise-robust prototype network named MProto for the DS-NER task.
722, The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values
Hannah Kirk; Andrew Bean; Bertie Vidgen; Paul Rottger; Scott Hale;
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Highlight: In this paper, we survey existing approaches for learning from human feedback, drawing on 95 papers primarily from the ACL and arXiv repositories.
723, TempTabQA: Temporal Question Answering for Semi-Structured Tables
Vivek Gupta; Pranshu Kandoi; Mahek Vora; Shuo Zhang; Yujie He; Ridho Reinanda; Vivek Srikumar;
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Highlight: Can current NLP systems reason about such information in semi-structured tables? To tackle this question, we introduce the task of temporal question answering on semi-structured tables.
724, Task-Level Thinking Steps Help Large Language Models for Challenging Classification Task
Chunhui Du; Jidong Tian; Haoran Liao; Jindou Chen; Hao He; Yaohui Jin;
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Highlight: In this paper, we propose the concept of task-level thinking steps that can eliminate bias introduced by demonstrations.
725, Influence Scores at Scale for Efficient Language Data Sampling
Nikhil Anand; Joshua Tan; Maria Minakova;
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Highlight: In this paper, we explore the applicability of influence scores in language classification tasks.
726, Analyzing Modular Approaches for Visual Question Decomposition
Apoorv Khandelwal; Ellie Pavlick; Chen Sun;
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Highlight: The latest such methods simultaneously introduce LLM-based code generation to build programs and a number of skill-specific, task-oriented modules to execute them. In this paper, we focus on ViperGPT and ask where its additional performance comes from and how much is due to the (state-of-art, end-to-end) BLIP-2 model it subsumes vs. additional symbolic components.
727, Improving Summarization with Human Edits
Zonghai Yao; Benjamin Schloss; Sai Selvaraj;
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Highlight: In this paper, we focus on a less explored form of human feedback ? Human Edits.
728, The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages
Chiyu Zhang; Khai Doan; Qisheng Liao; Muhammad Abdul-Mageed;
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Highlight: This deficiency arises partly from SM not being adequately represented in any of the existing benchmarks. To address this gap, we present SPARROW, an extensive multilingual benchmark specifically designed for SM understanding.
729, BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in Biology
Odhran O?Donoghue; Aleksandar Shtedritski; John Ginger; Ralph Abboud; Ali Ghareeb; Samuel Rodriques;
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Highlight: Here we present an automatic evaluation framework for the task of planning experimental protocols, and we introduce BioProt: a dataset of biology protocols with corresponding pseudocode representations.
730, Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts
Tengxiao Liu; Qipeng Guo; Yuqing Yang; Xiangkun Hu; Yue Zhang; Xipeng Qiu; Zheng Zhang;
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Highlight: In this work, we propose XoT, an integrated problem solving framework by prompting LLMs with diverse reasoning thoughts.
731, Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques
Manon Reusens; Philipp Borchert; Margot Mieskes; Jochen De Weerdt; Bart Baesens;
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Highlight: This paper investigates the transferability of debiasing techniques across different languages within multilingual models.
732, Exploring All-In-One Knowledge Distillation Framework for Neural Machine Translation
Zhongjian Miao; Wen Zhang; Jinsong Su; Xiang Li; Jian Luan; Yidong Chen; Bin Wang; Min Zhang;
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Highlight: In this work, we propose a novel All-In-One Knowledge Distillation(AIO-KD) framework for NMT, which generates multiple satisfactory students at once.
733, Generative Spoken Language Model Based on Continuous Word-sized Audio Tokens
Robin Algayres; Yossi Adi; Tu Nguyen; Jade Copet; Gabriel Synnaeve; Beno?t Sagot; Emmanuel Dupoux;
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Highlight: Taking inspiration from word-based LM, we introduce a Generative Spoken Language Model (GSLM) based on word-size continuous-valued audio tokens that can generate diverse and expressive language output.
734, Lion: Adversarial Distillation of Proprietary Large Language Models
Yuxin Jiang; Chunkit Chan; Mingyang Chen; Wei Wang;
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Highlight: To this end, we propose a novel adversarial distillation framework for a more efficient knowledge transfer.
735, Evaluating Large Language Models on Controlled Generation Tasks
Jiao Sun; Yufei Tian; Wangchunshu Zhou; Nan Xu; Qian Hu; Rahul Gupta; John Wieting; Nanyun Peng; Xuezhe Ma;
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Highlight: We present an extensive analysis of various benchmarks including a sentence planning benchmark with different granularities.
736, Why LLMs Hallucinate, and How to Get (Evidential) Closure: Perceptual, Intensional, and Extensional Learning for Faithful Natural Language Generation
Adam Bouyamourn;
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Highlight: We show that LLMs hallucinate because their output is not constrained to be synonymous with claims for which they have evidence: a condition that we call evidential closure.
737, Improving Chinese Pop Song and Hokkien Gezi Opera Singing Voice Synthesis By Enhancing Local Modeling
Peng Bai; Yue Zhou; Meizhen Zheng; Wujin Sun; Xiaodong Shi;
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Highlight: Consequently, the synthesized audio exhibits local incongruities (e. g. , local pronunciation jitter or local noise). To address this problem, we propose two methods to enhance local modeling in the acoustic model.
738, GROOViST: A Metric for Grounding Objects in Visual Storytelling
Aditya Surikuchi; Sandro Pezzelle; Raquel Fern?ndez;
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Highlight: In this work, we focus on evaluating the degree of grounding, that is, the extent to which a story is about the entities shown in the images.
739, TopWORDS-Poetry: Simultaneous Text Segmentation and Word Discovery for Classical Chinese Poetry Via Bayesian Inference
Changzai Pan; Feiyue Li; Ke Deng;
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Highlight: Little effort has been made in the literature for processing texts from classical Chinese poetry. This study fills in this gap with TopWORDS-Poetry, an unsupervised method that can achieve reliable text segmentation and word discovery for classical Chinese poetry simultaneously without pre-given vocabulary or training corpus.
740, Struct-XLM: A Structure Discovery Multilingual Language Model for Enhancing Cross-lingual Transfer Through Reinforcement Learning
Linjuan Wu; Weiming Lu;
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Highlight: Additionally, existing methods require syntactic labels that are difficult to obtain and of poor quality for low-resource languages. To address this gap, we propose Struct-XLM, a novel multilingual language model that leverages reinforcement learning (RL) to autonomously discover universal syntactic structures for improving the cross-lingual representation alignment of PLM.
741, Interview Evaluation: A Novel Approach for Automatic Evaluation of Conversational Question Answering Models
Xibo Li; Bowei Zou; Yifan Fan; Yanling Li; Ai Ti Aw; Yu Hong;
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Highlight: In this paper, we propose a novel automatic evaluation approach, interview evaluation.
742, TCFLE-8: A Corpus of Learner Written Productions for French As A Foreign Language and Its Application to Automated Essay Scoring
Rodrigo Wilkens; Alice Pintard; David Alfter; Vincent Folny; Thomas Fran?ois;
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Highlight: In this paper, we aim to foster the development of AES for French by providing the TCFLE-8 corpus, a corpus of 6.
743, Confidence-based Ensembling of Perspective-aware Models
Silvia Casola; Soda Lo; Valerio Basile; Simona Frenda; Alessandra Cignarella; Viviana Patti; Cristina Bosco;
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Highlight: In this paper, we propose a strongly perspectivist model for supervised classification of natural language utterances.
744, Adaptive Gating in Mixture-of-Experts Based Language Models
Jiamin Li; Qiang Su; Yitao Yang; Yimin Jiang; Cong Wang; Hong Xu;
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Highlight: This paper introduces adaptive gating in MoE, a flexible training strategy that allows tokens to be processed by a variable number of experts based on expert probability distribution.
745, On The Automatic Generation and Simplification of Children?s Stories
Maria Valentini; Jennifer Weber; Jesus Salcido; T?a Wright; Eliana Colunga; Katharina von der Wense;
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Highlight: Working toward the goal of age-appropriate simplicity in generated educational texts, we first examine the ability of several popular LLMs to generate stories with properly adjusted lexical and readability levels. We find that, in spite of the growing capabilities of LLMs, they do not yet possess the ability to limit their vocabulary to levels appropriate for younger age groups.
746, Retrofitting Light-weight Language Models for Emotions Using Supervised Contrastive Learning
Sapan Shah; Sreedhar Reddy; Pushpak Bhattacharyya;
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Highlight: We present a novel retrofitting method to induce emotion aspects into pre-trained language models (PLMs) such as BERT and RoBERTa.
747, Revisiting De-Identification of Electronic Medical Records: Evaluation of Within- and Cross-Hospital Generalization
Yiyang Liu; Jinpeng Li; Enwei Zhu;
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Highlight: This study introduces a new de-identification dataset comprising EMRs from three hospitals in China, creating a benchmark for evaluating both within- and cross-hospital generalization.
748, Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models
James Michaelov; Catherine Arnett; Tyler Chang; Ben Bergen;
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Highlight: We find evidence for abstract monolingual and crosslingual grammatical representations in the models that function similarly to those found in humans.
749, Deep Natural Language Feature Learning for Interpretable Prediction
Felipe Urrutia; Cristian Calderon; Valentin Barriere;
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Highlight: We propose a general method to break down a main complex task into a set of intermediary easier sub-tasks, which are formulated in natural language as binary questions related to the final target task.
750, ROBBIE: Robust Bias Evaluation of Large Generative Language Models
David Esiobu; Xiaoqing Tan; Saghar Hosseini; Megan Ung; Yuchen Zhang; Jude Fernandes; Jane Dwivedi-Yu; Eleonora Presani; Adina Williams; Eric Smith;
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Highlight: In this work, our focus is two-fold: (1) Benchmarking: a comparison of 6 different prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative LLMs.
751, Enhancing Task-oriented Dialogue Systems with Generative Post-processing Networks
Atsumoto Ohashi; Ryuichiro Higashinaka;
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Highlight: In this study, we propose a new post-processing component for NLG, generative post-processing networks (GenPPNs).
752, Selective Labeling: How to Radically Lower Data-Labeling Costs for Document Extraction Models
Yichao Zhou; James Wendt; Navneet Potti; Jing Xie; Sandeep Tata;
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Highlight: A key bottleneck in developing extraction models for new document types is the cost of acquiring the several thousand high-quality labeled documents that are needed to train a model with acceptable accuracy. In this paper, we propose selective labeling as a solution to this problem.
753, TRAVEL: Tag-Aware Conversational FAQ Retrieval Via Reinforcement Learning
Yue Chen; Dingnan Jin; Chen Huang; Jia Liu; Wenqiang Lei;
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Highlight: However, the conversation context contains noise, e. g. , users may click questions they don?t like, leading to inaccurate semantics modeling. To tackle this, we introduce tags of FAQ questions, which can help us eliminate irrelevant information.
754, Continual Dialogue State Tracking Via Example-Guided Question Answering
Hyundong Cho; Andrea Madotto; Zhaojiang Lin; Khyathi Chandu; Satwik Kottur; Jing Xu; Jonathan May; Chinnadhurai Sankar;
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Highlight: Motivated by the insight that dialogue state tracking (DST), a crucial component of dialogue systems that estimates the user?s goal as a conversation proceeds, is a simple natural language understanding task, we propose reformulating it as a bundle of granular example-guided question answering tasks to minimize the task shift between services and thus benefit continual learning.
755, COVID-19 Vaccine Misinformation in Middle Income Countries
Jongin Kim; Byeo Bak; Aditya Agrawal; Jiaxi Wu; Veronika Wirtz; Traci Hong; Derry Wijaya;
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Highlight: This paper introduces a multilingual dataset of COVID-19 vaccine misinformation, consisting of annotated tweets from three middle-income countries: Brazil, Indonesia, and Nigeria.
756, A Rose By Any Other Name Would Not Smell As Sweet: Social Bias in Names Mistranslation
Sandra Sandoval; Jieyu Zhao; Marine Carpuat; Hal Daum? III;
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Highlight: We analyze the effect of name demographics on translation quality using generalized linear mixed effects models and find that the ability of translation systems to correctly translate female-associated names is significantly lower than male-associated names.
757, Investigating Efficiently Extending Transformers for Long Input Summarization
Jason Phang; Yao Zhao; Peter Liu;
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Highlight: Based on our findings, we introduce PEGASUS-X, an extension of the PEGASUS model with additional long input pretraining to handle inputs of up to 16K tokens, which achieves strong performance on long input summarization tasks comparable with much larger models.
758, Unifying Cross-Lingual Transfer Across Scenarios of Resource Scarcity
Alan Ansell; Marinela Parovic; Ivan Vulic; Anna Korhonen; Edoardo Ponti;
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Highlight: However, the level of scarcity varies significantly across multiple dimensions, including: i) the amount of task-specific data available in the source and target languages; ii) the amount of monolingual and parallel data available for both languages; and iii) the extent to which they are supported by pretrained multilingual and translation models. Prior work has largely treated these dimensions and the various techniques for dealing with them separately; in this paper, we offer a more integrated view by exploring how to deploy the arsenal of cross-lingual transfer tools across a range of scenarios, especially the most challenging, low-resource ones.
759, DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining
Weifeng Jiang; Qianren Mao; Chenghua Lin; Jianxin Li; Ting Deng; Weiyi Yang; Zheng Wang;
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Highlight: We present DisCo, a semi-supervised learning (SSL) framework for fine-tuning a cohort of small student models generated from a large PLM using knowledge distillation.
760, Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection
Gretel De la Pe?a Sarrac?n; Paolo Rosso; Robert Litschko; Goran Glava?; Simone Ponzetto;
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Highlight: In this work, we resort to data augmentation and continual pre-training for domain adaptation to improve cross-lingual abusive language detection.
761, ULF: Unsupervised Labeling Function Correction Using Cross-Validation for Weak Supervision
Anastasiia Sedova; Benjamin Roth;
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Highlight: In this work, we investigate noise reduction techniques for WS based on the principle of k-fold cross-validation.
762, Ideology Takes Multiple Looks: A High-Quality Dataset for Multifaceted Ideology Detection
Songtao Liu; Ziling Luo; Minghua Xu; Lixiao Wei; Ziyao Wei; Han Yu; Wei Xiang; Bang Wang;
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Highlight: We construct a MITweet dataset for the MID task, which contains 12,594 English Twitter posts, each annotated with a Relevance and an Ideology label for all twelve facets.
763, Support or Refute: Analyzing The Stance of Evidence to Detect Out-of-Context Mis- and Disinformation
Xin Yuan; Jie Guo; Weidong Qiu; Zheng Huang; Shujun Li;
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Highlight: Motivated by the intuition that the stance of evidence represents a bias towards different detection results, we propose a stance extraction network (SEN) that can extract the stances of different pieces of multi-modal evidence in a unified framework.
764, Uncertainty Guided Global Memory Improves Multi-Hop Question Answering
Alsu Sagirova; Mikhail Burtsev;
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Highlight: However, attention-based token representations lack explicit global contextual information to connect reasoning steps. To address these issues, we propose GEMFormer, a two-stage method that first collects relevant information over the entire document to the memory and then combines it with local context to solve the task.
765, Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation
Yuanyuan Liang; Jianing Wang; Hanlun Zhu; Lei Wang; Weining Qian; Yunshi Lan;
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Highlight: Inspired by Chain-of-Thought (CoT) prompting, which is an in-context learning strategy for reasoning, we formulate KBQG task as a reasoning problem, where the generation of a complete question is splitted into a series of sub-question generation.
766, MingOfficial: A Ming Official Career Dataset and A Historical Context-Aware Representation Learning Framework
You-Jun Chen; Hsin-Yi Hsieh; Yu Lin; Yingtao Tian; Bert Chan; Yu-Sin Liu; Yi-Hsuan Lin; Richard Tsai;
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Highlight: By making MingOfficial publicly available (see main text for the URL) as both a dataset and an interactive tool, we aim to stimulate further research into the role of social context and representation learning in identifying individual characteristics, and hope to provide inspiration for computational approaches in other fields beyond Chinese studies.
767, DPP-TTS: Diversifying Prosodic Features of Speech Via Determinantal Point Processes
Seongho Joo; Hyukhun Koh; Kyomin Jung;
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Highlight: In this paper, we propose DPP-TTS: a text-to-speech model based on Determinantal Point Processes (DPPs) with a prosody diversifying module.
768, Interactive Text Generation
Felix Faltings; Michel Galley; Kiant? Brantley; Baolin Peng; Weixin Cai; Yizhe Zhang; Jianfeng Gao; Bill Dolan;
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Highlight: We introduce a new Interactive Text Generation task that allows training generation models interactively without the costs of involving real users, by using user simulators that provide edits that guide the model towards a given target text.
769, Knowledge Distillation ? Label Smoothing: Fact or Fallacy?
Md Sultan;
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Highlight: Perhaps the strongest argument of all for this new perspective comes from its apparent similarities with label smoothing (LS). Here we re-examine this stated equivalence between the two methods by comparing the predictive confidences of the models they train.
770, Analyzing Cognitive Plausibility of Subword Tokenization
Lisa Beinborn; Yuval Pinter;
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Highlight: We present a new evaluation paradigm that focuses on the cognitive plausibility of subword tokenization.
771, Can Large Language Models Capture Dissenting Human Voices?
Noah Lee; Na Min An; James Thorne;
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Highlight: In this paper, we evaluate the performance and alignment of LLM distribution with humans using two different techniques to estimate the multinomial distribution: Monte Carlo Estimation (MCE) and Log Probability Estimation (LPE).
772, DecoMT: Decomposed Prompting for Machine Translation Between Related Languages Using Large Language Models
Ratish Puduppully; Anoop Kunchukuttan; Raj Dabre; Ai Ti Aw; Nancy Chen;
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Highlight: We introduce DecoMT, a novel approach of few-shot prompting that decomposes the translation process into a sequence of word chunk translations.
773, GradSim: Gradient-Based Language Grouping for Effective Multilingual Training
Mingyang Wang; Heike Adel; Lukas Lange; Jannik Str?tgen; Hinrich Schuetze;
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Highlight: In this paper, we propose GradSim, a language grouping method based on gradient similarity.
774, Discovering Universal Geometry in Embeddings with ICA
Hiroaki Yamagiwa; Momose Oyama; Hidetoshi Shimodaira;
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Highlight: This study utilizes Independent Component Analysis (ICA) to unveil a consistent semantic structure within embeddings of words or images.
775, Toward A Critical Toponymy Framework for Named Entity Recognition: A Case Study of Airbnb in New York City
Mikael Brunila; Jack LaViolette; Sky CH-Wang; Priyanka Verma; Clara F?r?; Grant McKenzie;
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Highlight: Here, we develop computational methods to measure how cultural and economic capital shape the ways in which people refer to places, through a novel annotated dataset of 47,440 New York City Airbnb listings from the 2010s. Building on this dataset, we introduce a new named entity recognition (NER) model able to identify important discourse categories integral to the characterization of place.
776, Well Begun Is Half Done: Generator-agnostic Knowledge Pre-Selection for Knowledge-Grounded Dialogue
Lang Qin; Yao Zhang; Hongru Liang; Jun Wang; Zhenglu Yang;
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Highlight: We propose \tt{GATE}, a generator-agnostic knowledge selection method, to prepare knowledge for subsequent response generation models by selecting context-related knowledge among different knowledge structures and variable knowledge requirements.
777, Best of Both Worlds: Towards Improving Temporal Knowledge Base Question Answering Via Targeted Fact Extraction
Nithish Kannen; Udit Sharma; Sumit Neelam; Dinesh Khandelwal; Shajith Ikbal; Hima Karanam; L Subramaniam;
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Highlight: We model the extraction problem as an open-domain question answering task using off-the-shelf language models.
778, Text Fact Transfer
Nishant Balepur; Jie Huang; Kevin Chang;
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Highlight: Text style transfer is a prominent task that aims to control the style of text without inherently changing its factual content. To cover more text modification applications, such as adapting past news for current events and repurposing educational materials, we propose the task of text fact transfer, which seeks to transfer the factual content of a source text between topics without modifying its style.
779, Cross-Document Event Coreference Resolution on Discourse Structure
Xinyu Chen; Sheng Xu; Peifeng Li; Qiaoming Zhu;
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Highlight: In general, most of them only consider the local context of event mentions and ignore their implicit global information, thus failing to capture the interactions of long-distance event mentions. To address the above issue, we regard discourse structure as global information to further improve CD-ECR.
780, EDIS: Entity-Driven Image Search Over Multimodal Web Content
Siqi Liu; Weixi Feng; Tsu-Jui Fu; Wenhu Chen; William Wang;
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Highlight: In this work, we introduce Entity-Driven Image Search (EDIS), a challenging dataset for cross-modal image search in the news domain.
781, GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
Joshua Ainslie; James Lee-Thorp; Michiel de Jong; Yury Zemlyanskiy; Federico Lebron; Sumit Sanghai;
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Highlight: We (1) propose a recipe for uptraining existing multi-head language model checkpoints into models with MQA using 5% of original pre-training compute, and (2) introduce grouped-query attention (GQA), a generalization of multi-query attention which uses an intermediate (more than one, less than number of query heads) number of key-value heads.
782, Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs Through A Global Prompt Hacking Competition
Sander Schulhoff; Jeremy Pinto; Anaum Khan; Louis-Fran?ois Bouchard; Chenglei Si; Svetlina Anati; Valen Tagliabue; Anson Kost; Christopher Carnahan; Jordan Boyd-Graber;
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Highlight: Although widely acknowledged as a significant security threat, there is a dearth of a large-scale resource and quantitative study on prompt hacking. To address this lacuna, we launch a global prompt hacking competition, which allows for free-form human input attacks.
783, CoLT5: Faster Long-Range Transformers with Conditional Computation
Joshua Ainslie; Tao Lei; Michiel de Jong; Santiago Ontanon; Siddhartha Brahma; Yury Zemlyanskiy; David Uthus; Mandy Guo; James Lee-Thorp; Yi Tay; Yun-Hsuan Sung; Sumit Sanghai;
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Highlight: However, not all tokens are equally important, especially for longer documents. We propose CoLT5, a long-input Transformer model that builds on this intuition by employing conditional computation, devoting more resources to important tokens in both feedforward and attention layers.
784, DiSTRICT: Dialogue State Tracking with Retriever Driven In-Context Tuning
Praveen Venkateswaran; Evelyn Duesterwald; Vatche Isahagian;
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Highlight: In this work, we propose DiSTRICT, a generalizable in-context tuning approach for DST that retrieves highly relevant training examples for a given dialogue to fine-tune the model without any hand-crafted templates.
785, Cross-Cultural Analysis of Human Values, Morals, and Biases in Folk Tales
Winston Wu; Lu Wang; Rada Mihalcea;
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Highlight: Using a range of lexicons and correlation analyses, we examine how human values, morals, and gender biases are expressed in folk tales across cultures.
786, LINC: A Neurosymbolic Approach for Logical Reasoning By Combining Language Models with First-Order Logic Provers
Theo Olausson; Alex Gu; Ben Lipkin; Cedegao Zhang; Armando Solar-Lezama; Joshua Tenenbaum; Roger Levy;
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Highlight: While many prompting-based strategies have been proposed to enable Large Language Models (LLMs) to do such reasoning more effectively, they still appear unsatisfactory, often failing in subtle and unpredictable ways. In this work, we investigate the validity of instead reformulating such tasks as modular neurosymbolic programming, which we call LINC: Logical Inference via Neurosymbolic Computation.
787, ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing
Nam Nguyen; Thang Phan; Duc-Vu Nguyen; Kiet Nguyen;
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Highlight: In this paper, we present the first monolingual pre-trained language model for Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality and diverse Vietnamese social media texts using XLM-R architecture.
788, Cross-Modal Conceptualization in Bottleneck Models
Danis Alukaev; Semen Kiselev; Ilya Pershin; Bulat Ibragimov; Vladimir Ivanov; Alexey Kornaev; Ivan Titov;
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Highlight: In our approach, we adopt a more moderate assumption and instead use text descriptions (e. g. , radiology reports), accompanying the images, to guide the induction of concepts.
789, DREAM: Deployment of Recombination and Ensembles in Argument Mining
Florian Ruosch; Cristina Sarasua; Abraham Bernstein;
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Highlight: This paper, in contrast, aims to provide a solution to achieve increased performance based on current components instead of independent all-new solutions.
790, COHESENTIA: A Novel Benchmark of Incremental Versus Holistic Assessment of Coherence in Generated Texts
Aviya Maimon; Reut Tsarfaty;
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Highlight: In this paper, we introduce CoheSentia, a novel benchmark of human-perceived coherence of automatically generated texts.
791, PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering Via Pluggable Reward-Driven Contextual Adapter
Haoyan Yang; Zhitao Li; Yong Zhang; Jianzong Wang; Ning Cheng; Ming Li; Jing Xiao;
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Highlight: Incorporating Large Language Models (LLMs) as generators is beneficial due to their advanced QA capabilities, but they are typically too large to be fine-tuned with budget constraints while some of them are only accessible via APIs. To tackle this issue and further improve ReQA performance, we propose a trainable Pluggable Reward-Driven Contextual Adapter (PRCA), keeping the generator as a black box.
792, Representative Demonstration Selection for In-Context Learning with Two-Stage Determinantal Point Process
Zhao Yang; Yuanzhe Zhang; Dianbo Sui; Cao Liu; Jun Zhao; Kang Liu;
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Highlight: Existing methods tend to select different demonstrations for each test instance, which is time-consuming and poses limitations in practical scenarios. Therefore, this study aims to address the challenge of selecting a representative subset of in-context demonstrations that can effectively prompt different test instances in a specific task.
793, The Effect of Scaling, Retrieval Augmentation and Form on The Factual Consistency of Language Models
Lovisa Hagstr?m; Denitsa Saynova; Tobias Norlund; Moa Johansson; Richard Johansson;
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Highlight: In this work, we identify potential causes of inconsistency and evaluate the effectiveness of two mitigation strategies: up-scaling and augmenting the LM with a passage retrieval database.
794, ViPE: Visualise Pretty-much Everything
Hassan Shahmohammadi; Adhiraj Ghosh; Hendrik Lensch;
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Highlight: Recent works primarily deal with this issue by compiling humanly annotated datasets on a small scale, which not only demands specialized expertise but also proves highly inefficient. To address this issue, we introduce ViPE: Visualise Pretty-much Everything.
795, EntSUMv2: Dataset, Models and Evaluation for More Abstractive Entity-Centric Summarization
Dhruv Mehra; Lingjue Xie; Ella Hofmann-Coyle; Mayank Kulkarni; Daniel Preotiuc-Pietro;
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Highlight: This paper presents ENTSUMV2, a more abstractive version of the original entity-centric ENTSUM summarization dataset.
796, Generating Commonsense Counterfactuals for Stable Relation Extraction
Xin Miao; Yongqi Li; Tieyun Qian;
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Highlight: Specifically, to identify causal terms accurately, we introduce an intervention-based strategy and leverage a constituency parser for correction.
797, Rumor Detection on Social Media with Crowd Intelligence and ChatGPT-Assisted Networks
Chang Yang; Peng Zhang; Wenbo Qiao; Hui Gao; Jiaming Zhao;
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Highlight: Nonetheless, existing research on rumor detection confronts several challenges: the limited expressive power of text encoding sequences, difficulties in domain knowledge coverage and effective information extraction with knowledge graph-based methods, and insufficient mining of semantic structural information. To address these issues, we propose a Crowd Intelligence and ChatGPT-Assisted Network(CICAN) for rumor classification.
798, Controllable Contrastive Generation for Multilingual Biomedical Entity Linking
Tiantian Zhu; Yang Qin; Qingcai Chen; Xin Mu; Changlong Yu; Yang Xiang;
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Highlight: In this paper, we propose Con2GEN, a prompt-based controllable contrastive generation framework for MBEL, which summarizes multidimensional information of the UMLS concept mentioned in biomedical text into a natural sentence following a predefined template.
799, HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts
Truong Do; Le Khiem; Quang Pham; TrungTin Nguyen; Thanh-Nam Doan; Binh Nguyen; Chenghao Liu; Savitha Ramasamy; Xiaoli Li; Steven Hoi;
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Highlight: However, this strategy has two key limitations: (i) the policy derived from random routers might be sub-optimal, and (ii) it requires extensive resources during training and evaluation, leading to limited efficiency gains. This work introduces HyperRouter, which dynamically generates the router?s parameters through a fixed hypernetwork and trainable embeddings to achieve a balance between training the routers and freezing them to learn an improved routing policy.
800, MediaHG: Rethinking Eye-catchy Features in Social Media Headline Generation
Boning Zhang; Yang Yang;
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Highlight: In this paper, we propose a disentanglement-based headline generation model: MediaHG (Social Media Headline Generation), which can balance the content and contextual features.
801, Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing Over Wikidata
Silei Xu; Shicheng Liu; Theo Culhane; Elizaveta Pertseva; Meng-Hsi Wu; Sina Semnani; Monica Lam;
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Highlight: This paper presents WikiWebQuestions, a high-quality question answering benchmark for Wikidata.
802, Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule
Andrey Bout; Alexander Podolskiy; Sergey Nikolenko; Irina Piontkovskaya;
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Highlight: In this work, we explore an orthogonal direction: how to use available data more efficiently.
803, The BLA Benchmark: Investigating Basic Language Abilities of Pre-Trained Multimodal Models
Xinyi Chen; Raquel Fern?ndez; Sandro Pezzelle;
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Highlight: Despite the impressive performance achieved by pre-trained language-and-vision models in downstream tasks, it remains an open question whether this reflects a proper understanding of image-text interaction. In this work, we explore to what extent they handle basic linguistic constructions?active-passive voice, coordination, and relative clauses?that even preschool children can typically master.
804, KEPL: Knowledge Enhanced Prompt Learning for Chinese Hypernym-Hyponym Extraction
Ningchen Ma; Dong Wang; Hongyun Bao; Lei He; Suncong Zheng;
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Highlight: Due to the flexibility of language expression and the lack of high-quality Chinese annotation datasets, it is still a challenge to accurately identify such relations from Chinese unstructured texts. To tackle this problem, we propose a Knowledge Enhanced Prompt Learning (KEPL) method for Chinese hypernym-hyponym relation extraction.
805, Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings
Qian Chen; Wen Wang; Qinglin Zhang; Siqi Zheng; Chong Deng; Hai Yu; Jiaqing Liu; Yukun Ma; Chong Zhang;
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Highlight: Our analysis reveals that the sentence embeddings from BERT suffer from a bias towards uninformative words, limiting the performance in semantic textual similarity (STS) tasks. To address this bias, we propose a simple and efficient unsupervised approach, Diagonal Attention Pooling (Ditto), which weights words with model-based importance estimations and computes the weighted average of word representations from pre-trained models as sentence embeddings.
806, Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction
Ji Qi; Chuchun Zhang; Xiaozhi Wang; Kaisheng Zeng; Jifan Yu; Jinxin Liu; Lei Hou; Juanzi Li; Xu Bin;
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Highlight: In this paper, we present the first benchmark that simulates the evaluation of open information extraction models in the real world, where the syntactic and expressive distributions under the same knowledge meaning may drift variously.
807, Multi-level Contrastive Learning for Script-based Character Understanding
Dawei Li; Hengyuan Zhang; Yanran Li; Shiping Yang;
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Highlight: In this work, we tackle the scenario of understanding characters in scripts, which aims to learn the characters? personalities and identities from their utterances.
808, Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks
Ramon Ruiz-Dolz; Stella Heras; Ana Garcia;
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Highlight: Such is the case of the automatic evaluation of complete professional argumentative debates. In this paper, we propose an original hybrid method to automatically predict the winning stance in this kind of debates.
809, Learning to Rank Generation with Pairwise Partial Rewards
Youngwon Lee; Jinu Lee; Seung-won Hwang;
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Highlight: However, it still suffers from challenges including the large action space and the delayed reward, as the reward can be computed only after an entire sequence is generated. To address these challenges, we propose a method that provides partial rewards for intermediate actions taken on partial sequences.
810, GreedyCAS: Unsupervised Scientific Abstract Segmentation with Normalized Mutual Information
Yingqiang Gao; Jessica Lam; Nianlong Gu; Richard Hahnloser;
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Highlight: In this work, we explore Normalized Mutual Information (NMI) as a means for abstract segmentation.
811, Multimodal Embodied Plan Prediction Augmented with Synthetic Embodied Dialogue
Aishwarya Padmakumar; Mert Inan; Spandana Gella; Patrick Lange; Dilek Hakkani-Tur;
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Highlight: Embodied task completion is a challenge where an agent in a simulated environment must predict environment actions to complete tasks based on natural language instructions and ego-centric visual observations. We propose a variant of this problem where the agent predicts actions at a higher level of abstraction called a plan, which helps make agent actions more interpretable and can be obtained from the appropriate prompting of large language models.
812, Abstractive Open Information Extraction
Kevin Pei; Ishan Jindal; Kevin Chang;
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Highlight: In this paper, we broaden the scope of OpenIE relations from merely the surface form of relations to include inferred relations, which we term abstractive OpenIE.
813, Dynamic Top-k Estimation Consolidates Disagreement Between Feature Attribution Methods
Jonathan Kamp; Lisa Beinborn; Antske Fokkens;
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Highlight: In this work, we propose a way to determine the number of optimal k tokens that should be displayed from sequential properties of the attribution scores.
814, SentiStream: A Co-Training Framework for Adaptive Online Sentiment Analysis in Evolving Data Streams
Yuhao Wu; Karthick Sharma; Chun Seah; Shuhao Zhang;
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Highlight: This paper presents sentistream, a novel co-training framework specifically designed for efficient sentiment analysis within dynamic data streams.
815, Modeling Empathic Similarity in Personal Narratives
Jocelyn Shen; Maarten Sap; Pedro Colon-Hernandez; Hae Park; Cynthia Breazeal;
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Highlight: We introduce a new task of identifying similarity in personal stories based on empathic resonance, i. e. , the extent to which two people empathize with each others? experiences, as opposed to raw semantic or lexical similarity, as has predominantly been studied in NLP.
816, Empathy Intent Drives Empathy Detection
Liting Jiang; Di Wu; Bohui Mao; Yanbing Li; Wushour Slamu;
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Highlight: To make joint training of the two tasks more challenging, we propose a novel framework, Cascaded Label Signal Network, which uses the cascaded interactive attention module and the label signal enhancement module to capture feature exchange information between empathy and empathy intent representations.
817, Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling
Yuanjun Shi; Linzhi Wu; Minglai Shao;
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Highlight: Considering simplicity, efficiency and generalizability, we present a cascade-style joint learning framework coupled with context-aware soft label representations and slot-level contrastive representation learning to mitigate the data and label shift problems effectively.
818, BasahaCorpus: An Expanded Linguistic Resource for Readability Assessment in Central Philippine Languages
Joseph Imperial; Ekaterina Kochmar;
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Highlight: In this work, we introduce and release BasahaCorpus as part of an initiative aimed at expanding available corpora and baseline models for readability assessment in lower resource languages in the Philippines.
819, Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understanding
Shuwen Deng; Paul Prasse; David Reich; Tobias Scheffer; Lena J?ger;
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Highlight: We develop a model that integrates synthetic scanpath generation with a scanpath-augmented language model, eliminating the need for human gaze data.
820, Adapt in Contexts: Retrieval-Augmented Domain Adaptation Via In-Context Learning
Quanyu Long; Wenya Wang; Sinno Pan;
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Highlight: In this paper, we study the UDA problem under an in-context learning setting to adapt language models from the source domain to the target domain without any target labels.
821, Efficient Classification of Long Documents Via State-Space Models
Peng Lu; Suyuchen Wang; Mehdi Rezagholizadeh; Bang Liu; Ivan Kobyzev;
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Highlight: Instead of tackling the computation difficulty for self-attention with sparse or hierarchical structures, in this paper, we investigate the use of State-Space Models (SSMs) for long document classification tasks.
822, Construction Artifacts in Metaphor Identification Datasets
Joanne Boisson; Luis Espinosa-Anke; Jose Camacho-Collados;
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Highlight: However, in this paper we show how existing metaphor identification datasets can be gamed by fully ignoring the potential metaphorical expression or the context in which it occurs. We test this hypothesis in a variety of datasets and settings, and show that metaphor identification systems based on language models without complete information can be competitive with those using the full context.
823, Granularity Matters: Pathological Graph-driven Cross-modal Alignment for Brain CT Report Generation
Yanzhao Shi; Junzhong Ji; Xiaodan Zhang; Liangqiong Qu; Ying Liu;
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Highlight: In this paper, we propose a novel Pathological Graph-driven Cross-modal Alignment (PGCA) model for accurate and robust Brain CT report generation.
824, A Fair and In-Depth Evaluation of Existing End-to-End Entity Linking Systems
Hannah Bast; Matthias Hertel; Natalie Prange;
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Highlight: We provide a more meaningful and fair in-depth evaluation of a variety of existing end-to-end entity linkers.
825, A Multi-Task Dataset for Assessing Discourse Coherence in Chinese Essays: Structure, Theme, and Logic Analysis
Hongyi Wu; Xinshu Shen; Man Lan; Shaoguang Mao; Xiaopeng Bai; Yuanbin Wu;
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Highlight: This paper introduces the Chinese Essay Discourse Coherence Corpus (CEDCC), a multi-task dataset for assessing discourse coherence.
826, SKD-NER: Continual Named Entity Recognition Via Span-based Knowledge Distillation with Reinforcement Learning
Yi Chen; Liang He;
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Highlight: However, the current strategies fall short of effectively addressing the catastrophic forgetting of previously learned entity types. To tackle this issue, we propose the SKD-NER model, an efficient continual learning NER model based on the span-based approach, which innovatively incorporates reinforcement learning strategies to enhance the model?s ability against catastrophic forgetting.
827, Lazy-k Decoding: Constrained Decoding for Information Extraction
Arthur Hemmer; Mickael Coustaty; Nicola Bartolo; Jerome Brachat; Jean-marc Ogier;
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Highlight: We explore the possibility of improving probabilistic models in structured prediction.
828, Personalized Distillation: Empowering Open-Sourced LLMs with Adaptive Learning for Code Generation
Hailin Chen; Amrita Saha; Steven Hoi; Shafiq Joty;
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Highlight: Inspired by modern teaching principles, we design a personalised distillation process, in which the student attempts to solve a task first, then the teacher provides an adaptive refinement for the student to improve.
829, Do Language Models Have A Common Sense Regarding Time? Revisiting Temporal Commonsense Reasoning in The Era of Large Language Models
Raghav Jain; Daivik Sojitra; Arkadeep Acharya; Sriparna Saha; Adam Jatowt; Sandipan Dandapat;
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Highlight: We critically evaluate 8 different LLMs across 6 datasets using 3 distinct prompting strategies.
830, Evaluation of African American Language Bias in Natural Language Generation
Nicholas Deas; Jessica Grieser; Shana Kleiner; Desmond Patton; Elsbeth Turcan; Kathleen McKeown;
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Highlight: We measure large language model performance on two tasks: a counterpart generation task, where a model generates AAL given WME and vice versa, and a masked span prediction (MSP) task, where models predict a phrase hidden from their input. Using a novel dataset of AAL texts from a variety of regions and contexts, we present evidence of dialectal bias for six pre-trained LLMs through performance gaps on these tasks.
831, Cognate Transformer for Automated Phonological Reconstruction and Cognate Reflex Prediction
V.S.D.S.Mahesh Akavarapu; Arnab Bhattacharya;
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Highlight: Several ideas and techniques drawn from computational biology have been successfully applied in this area of computational historical linguistics. Following these lines, we adapt MSA Transformer, a protein language model, to the problem of automated phonological reconstruction.
832, Weakly Supervised Semantic Parsing with Execution-based Spurious Program Filtering
Kang-il Lee; Segwang Kim; Kyomin Jung;
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Highlight: In this paper, we propose a domain-agnostic filtering mechanism based on program execution results.
833, Rather A Nurse Than A Physician - Contrastive Explanations Under Investigation
Oliver Eberle; Ilias Chalkidis; Laura Cabello; Stephanie Brandl;
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Highlight: Thus, we empirically find that humans do not necessarily explain in a contrastive manner.
834, An Investigation of LLMs? Inefficacy in Understanding Converse Relations
Chengwen Qi; Bowen Li; Binyuan Hui; Bailin Wang; Jinyang Li; Jinwang Wu; Yuanjun Laili;
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Highlight: Therefore, a natural question rises that do LLMs really understand the structured semantics of formal languages. In this paper, we investigate this problem on a special case, converse binary relation.
835, Towards Low-Resource Automatic Program Repair with Meta-Learning and Pretrained Language Models
Weishi Wang; Yue Wang; Steven Hoi; Shafiq Joty;
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Highlight: However, in practical scenarios, software bugs have an imbalanced distribution, and the fixing knowledge learned by APR models often only capture the patterns of frequent error types, making it inapplicable to handle the rare error types. To address this limitation, we investigate a novel task of low-resource APR, and propose Meta-APR, a new meta-learning framework integrated with code pretrained language models to generate fixes for low-resource bugs with limited training samples.
836, Log-FGAER: Logic-Guided Fine-Grained Address Entity Recognition from Multi-Turn Spoken Dialogue
Xue Han; Yitong Wang; Qian Hu; Pengwei Hu; Chao Deng; Junlan Feng;
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Highlight: This challenge has not been well emphasized by main-stream entity extraction algorithms. To address this issue, we propose in this paper a logic-guided fine-grained address recognition method (Log-FGAER), where we formulate the address hierarchy relationship as the logic rule and softly apply it in a probabilistic manner to improve the accuracy of FGAER.
837, Unified Low-Resource Sequence Labeling By Sample-Aware Dynamic Sparse Finetuning
Sarkar Snigdha Sarathi Das; Haoran Zhang; Peng Shi; Wenpeng Yin; Rui Zhang;
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Highlight: This significantly bounds its usefulness in data-limited settings where finetuning large models cannot properly generalize to the target format. To address this challenge and leverage PLM knowledge effectively, we propose FISH-DIP, a sample-aware dynamic sparse finetuning strategy that selectively focuses on a fraction of parameters, informed by feedback from highly regressing examples, during the fine-tuning process.
838, Non-autoregressive Text Editing with Copy-aware Latent Alignments
Yu Zhang; Yue Zhang; Leyang Cui; Guohong Fu;
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Highlight: Despite promising results, Seq2Edit approaches still face several challenges such as inflexibility in generation and difficulty in generalizing to other languages. In this work, we propose a novel non-autoregressive text editing method to circumvent the above issues, by modeling the edit process with latent CTC alignments.
839, Translating Away Translationese Without Parallel Data
Rricha Jalota; Koel Chowdhury; Cristina Espa?a-Bonet; Josef van Genabith;
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Highlight: In this paper, we explore a novel approach to reduce translationese in translated texts: translation-based style transfer.
840, HiddenTables and PyQTax: A Cooperative Game and Dataset For TableQA to Ensure Scale and Data Privacy Across A Myriad of Taxonomies
William Watson; Nicole Cho; Tucker Balch; Manuela Veloso;
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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-35-turbo. We propose a cooperative game dubbed ?HiddenTables? as a potential resolution to this challenge.
841, Accented Speech Recognition With Accent-specific Codebooks
Darshan Prabhu; Preethi Jyothi; Sriram Ganapathy; Vinit Unni;
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Highlight: In this work, we propose a novel accent adaptation approach for end-to-end ASR systems using cross-attention with a trainable set of codebooks.
842, Linking Surface Facts to Large-Scale Knowledge Graphs
Gorjan Radevski; Kiril Gashteovski; Chia-Chien Hung; Carolin Lawrence; Goran Glava?;
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Highlight: To bridge this gap, we need the best of both worlds: (i) high coverage of free-text OIEs, and (ii) semantic precision (i. e. , monosemy) of KGs. In order to achieve this goal, we propose a new benchmark with novel evaluation protocols that can, for example, measure fact linking performance on a granular triple slot level, while also measuring if a system has the ability to recognize that a surface form has no match in the existing KG.
843, Sentiment Analysis on Streaming User Reviews Via Dual-Channel Dynamic Graph Neural Network
Xin Zhang; Linhai Zhang; Deyu Zhou;
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Highlight: In this paper, we present DC-DGNN, a dual-channel framework based on a dynamic graph neural network (DGNN) that models temporal user and product dynamics for sentiment analysis.
844, DUMB: A Benchmark for Smart Evaluation of Dutch Models
Wietse de Vries; Martijn Wieling; Malvina Nissim;
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Highlight: We introduce the Dutch Model Benchmark: DUMB.
845, A Fine-Grained Taxonomy of Replies to Hate Speech
Xinchen Yu; Ashley Zhao; Eduardo Blanco; Lingzi Hong;
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Highlight: In this paper, we present a theoretically grounded taxonomy of replies to hate speech and a new corpus.
846, A Study on Accessing Linguistic Information in Pre-Trained Language Models By Using Prompts
Marion Di Marco; Katharina H?mmerl; Alexander Fraser;
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Highlight: We use the technique of prompting and formulate linguistic tasks to test the LM?s access to explicit grammatical principles and study how effective this method is at providing access to linguistic features.
847, Somali Information Retrieval Corpus: Bridging The Gap Between Query Translation and Dedicated Language Resources
Abdisalam Badel; Ting Zhong; Wenxin Tai; Fan Zhou;
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Highlight: We explain how the corpus was constructed, and develop a Somali language information retrieval system using a pseudo-relevance feedback (PRF) query expansion technique on the corpus.
848, Beat LLMs at Their Own Game: Zero-Shot LLM-Generated Text Detection Via Querying ChatGPT
Biru Zhu; Lifan Yuan; Ganqu Cui; Yangyi Chen; Chong Fu; Bingxiang He; Yangdong Deng; Zhiyuan Liu; Maosong Sun; Ming Gu;
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Highlight: In this work, we design a zero-shot black-box method for detecting LLM-generated texts.
849, Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks
Zhaohui Yan; Songlin Yang; Wei Liu; Kewei Tu;
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Highlight: In this work, we propose HyperGraph neural network for ERE (HGERE), which is built upon the PL-marker (a state-of-the-art marker-based pipleline model).
850, StrAE: Autoencoding for Pre-Trained Embeddings Using Explicit Structure
Mattia Opper; Victor Prokhorov; Siddharth N;
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Highlight: This work presents StrAE: a Structured Autoencoder framework that through strict adherence to explicit structure, and use of a novel contrastive objective over tree-structured representations, enables effective learning of multi-level representations.
851, RoBoCoP: A Comprehensive ROmance BOrrowing COgnate Package and Benchmark for Multilingual Cognate Identification
Liviu Dinu; Ana Uban; Alina Cristea; Anca Dinu; Ioan-Bogdan Iordache; Simona Georgescu; Laurentiu Zoicas;
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Highlight: In this paper we introduce a comprehensive database of Romance cognates and borrowings based on the etymological information provided by the dictionaries.
852, Instructive Dialogue Summarization with Query Aggregations
Bin Wang; Zhengyuan Liu; Nancy Chen;
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Highlight: To overcome the scarcity of instructive dialogue summarization data, we propose a three-step approach to synthesize high-quality query-based summarization triples.
853, Semantic Matching for Text Classification with Complex Class Descriptions
Brian De Silva; Kuan-Wen Huang; Gwang Lee; Karen Hovsepian; Yan Xu; Mingwei Shen;
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Highlight: Further, prior work is aimed at concise class descriptions, which may be insufficient for complex classes. We overcome these shortcomings by casting text classification as a matching problem, where a model matches examples with relevant class descriptions.
854, GLEN: Generative Retrieval Via Lexical Index Learning
Sunkyung Lee; Minjin Choi; Jongwuk Lee;
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Highlight: While it takes advantage of bypassing the construction of auxiliary index structures, existing studies face two significant challenges: (i) the discrepancy between the knowledge of pre-trained language models and identifiers and (ii) the gap between training and inference that poses difficulty in learning to rank. To overcome these challenges, we propose a novel generative retrieval method, namely Generative retrieval via LExical iNdex learning (GLEN).
855, Turn-Level Active Learning for Dialogue State Tracking
Zihan Zhang; Meng Fang; Fanghua Ye; Ling Chen; Mohammad-Reza Namazi-Rad;
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Highlight: In this paper, we propose a novel turn-level active learning framework for DST to actively select turns in dialogues to annotate.
856, Modeling Conceptual Attribute Likeness and Domain Inconsistency for Metaphor Detection
Yuan Tian; Nan Xu; Wenji Mao; Daniel Zeng;
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Highlight: Under the guidance of conceptual metaphor theory, in this paper, we model the likeness of attribute for the first time and propose a novel Attribute Likeness and Domain Inconsistency Learning framework (AIDIL) for word-pair metaphor detection.
857, Referring Image Segmentation Via Joint Mask Contextual Embedding Learning and Progressive Alignment Network
Ziling Huang; Shin?ichi Satoh;
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Highlight: However, its defects also obvious: existing methods within the cascade framework may encounter challenges in both maintaining a strong focus on the most relevant information during specific stages of the referring image segmentation process and rectifying errors propagated from early stages, which can ultimately result in sub-optimal performance. To address these limitations, we propose the Joint Mask Contextual Embedding Learning Network (JMCELN).
858, Scalable-DSC: A Structural Template Prompt Approach to Scalable Dialogue State Correction
Haoxiang Su; Hongyan Xie; Hao Huang; Shuangyong Song; Ruiyu Fang; Xiaomeng Huang; Sijie Feng;
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Highlight: These approaches, though effective, are heavily intertwined with specific DST models, limiting their applicability to other DST models. To solve this problem, we propose Scalable Dialogue State Correction (Scalable-DSC), which can correct wrong slot values in the dialogue state predicted by any DST model.
859, Don?t Trust ChatGPT When Your Question Is Not in English: A Study of Multilingual Abilities and Types of LLMs
Xiang Zhang; Senyu Li; Bradley Hauer; Ning Shi; Grzegorz Kondrak;
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Highlight: In this work, we propose a systematic way of qualitatively and quantitatively evaluating the multilingual capabilities of LLMs.
860, Empirical Study of Zero-Shot NER with ChatGPT
Tingyu Xie; Qi Li; Jian Zhang; Yan Zhang; Zuozhu Liu; Hongwei Wang;
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Highlight: Inspired by the remarkable reasoning capability of LLM on symbolic and arithmetic reasoning, we adapt the prevalent reasoning methods to NER and propose reasoning strategies tailored for NER.
861, Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation
Chenxu Yang; Zheng Lin; Lanrui Wang; Chong Tian; Liang Pang; Jiangnan Li; Qirong Ho; Yanan Cao; Weiping Wang;
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Highlight: In this work, we first find that such copying-style degeneration is primarily due to the weak likelihood objective, which allows the model to ?cheat? the objective by merely duplicating knowledge segments in a superficial pattern matching based on overlap. To overcome this challenge, we then propose a Multi-level Adaptive Contrastive Learning (MACL) framework that dynamically samples negative examples and subsequently penalizes degeneration behaviors at both the token-level and sequence-level.
862, A Diffusion Weighted Graph Framework for New Intent Discovery
Wenkai Shi; Wenbin An; Feng Tian; Qinghua Zheng; QianYing Wang; Ping Chen;
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Highlight: Without considering structure relationships between samples, previous methods generate noisy supervisory signals which cannot strike a balance between quantity and quality, hindering the formation of new intent clusters and effective transfer of the pre-training knowledge. To mitigate this limitation, we propose a novel Diffusion Weighted Graph Framework (DWGF) to capture both semantic similarities and structure relationships inherent in data, enabling more sufficient and reliable supervisory signals.
863, A Self-enhancement Multitask Framework for Unsupervised Aspect Category Detection
Thi-Nhung Nguyen; Hoang Ngo; Kiem-Hieu Nguyen; Tuan-Dung Cao;
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Highlight: However, aspect representations are limited by the quality of initial seed words, and model performances are compromised by noise. To mitigate this limitation, we propose a simple framework that automatically enhances the quality of initial seed words and selects high-quality sentences for training instead of using the entire dataset.
864, Find-2-Find: Multitask Learning for Anaphora Resolution and Object Localization
Cennet Oguz; Pascal Denis; Emmanuel Vincent; Simon Ostermann; Josef van Genabith;
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Highlight: In this paper, we present Find2Find, a joint anaphora resolution and object localization dataset targeting the problem of visual-linguistic ambiguity, consisting of 500 anaphora-annotated recipes with corresponding videos.
865, Background Summarization of Event Timelines
Adithya Pratapa; Kevin Small; Markus Dreyer;
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Highlight: While journalists often curate timelines to highlight key sub-events, newcomers to a news event face challenges in catching up on its historical context. In this paper, we address this need by introducing the task of background news summarization, which complements each timeline update with a background summary of relevant preceding events.
866, How Do Large Language Models Capture The Ever-changing World Knowledge? A Review of Recent Advances
Zihan Zhang; Meng Fang; Ling Chen; Mohammad-Reza Namazi-Rad; Jun Wang;
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Highlight: This paper provides a comprehensive review of recent advances in aligning deployed LLMs with the ever-changing world knowledge.
867, PreWoMe: Exploiting Presuppositions As Working Memory for Long Form Question Answering
Wookje Han; Jinsol Park; Kyungjae Lee;
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Highlight: In this work, we propose PreWoMe, a unified approach capable of handling any type of information-seeking question.
868, Memorisation Cartography: Mapping Out The Memorisation-Generalisation Continuum in Neural Machine Translation
Verna Dankers; Ivan Titov; Dieuwke Hupkes;
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Highlight: What determines a datapoint?s position on that spectrum, and how does that spectrum influence neural models? performance? We address these two questions for neural machine translation (NMT) models.
869, FedID: Federated Interactive Distillation for Large-Scale Pretraining Language Models
Xinge Ma; Jiangming Liu; Jin Wang; Xuejie Zhang;
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Highlight: Federated distillation (FD) is proposed to alleviate these limitations, but its performance is faded by confirmation bias. To tackle this issue, we propose Federated Interactive Distillation (FedID), which utilizes a small amount of labeled data retained by the server to further rectify the local models during knowledge transfer.
870, This Is Not A Dataset: A Large Negation Benchmark to Challenge Large Language Models
Iker Garc?a-Ferrero; Bego?a Altuna; Javier Alvez; Itziar Gonzalez-Dios; German Rigau;
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Highlight: We introduce a large semi-automatically generated dataset of circa 400,000 descriptive sentences about commonsense knowledge that can be true or false in which negation is present in about 2/3 of the corpus in different forms.
871, CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset
Susanna R?cker; Alan Akbik;
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Highlight: This poses challenges to objectively comparing NER approaches and analyzing their errors, as current state-of-the-art models achieve F1-scores that are comparable to or even exceed the estimated noise level in CoNLL-03. To address this issue, we present a comprehensive relabeling effort assisted by automatic consistency checking that corrects 7.
872, Not All Quantifiers Are Equal: Probing Transformer-based Language Models? Understanding of Generalised Quantifiers
Tharindu Madusanka; Iqra Zahid; Hao Li; Ian Pratt-Hartmann; Riza Batista-Navarro;
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Highlight: Consequently, they have not answered the aforementioned question faithfully or adequately. Therefore, we investigate how different generalised quantifiers affect TLMs by employing a textual entailment problem defined in a purely logical sense, namely, model-checking with natural language.
873, Seeing Through The Mess: Evolutionary Dynamics of Lexical Polysemy
Andreas Baumann; Andreas Stephan; Benjamin Roth;
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Highlight: In this work, we propose and analyze a mathematical model of the evolution of lexical meaning to investigate mechanisms leading to polysemy.
874, Are Embedded Potatoes Still Vegetables? On The Limitations of WordNet Embeddings for Lexical Semantics
Xuyou Cheng; Michael Schlichtkrull; Guy Emerson;
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Highlight: In this paper, we investigate the potential disconnect between the performance of KBE models of WordNet on link prediction and their ability to encode semantic information, highlighting the limitations of current evaluation protocols.
875, Event-Location Tracking in Narratives: A Case Study on Holocaust Testimonies
Eitan Wagner; Renana Keydar; Omri Abend;
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Highlight: This work focuses on the spatial dimension of narrative understanding and presents the task of event-location tracking in narrative texts.
876, Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources
Yerin Hwang; Yongil Kim; Hyunkyung Bae; Hwanhee Lee; Jeesoo Bang; Kyomin Jung;
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Highlight: However, the original dialog inpainting model is trained solely on the dialog reconstruction task, resulting in the generation of questions with low contextual relevance due to insufficient learning of question-answer alignment. To overcome this limitation, we propose a novel framework called Dialogizer, which has the capability to automatically generate ConvQA datasets with high contextual relevance from textual sources.
877, Learning to Predict Task Transferability Via Soft Prompt
Lingyun Feng;
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Highlight: In this work, we propose to learn an affinity scoring function to predict transferability between tasks.
878, Unnatural Error Correction: GPT-4 Can Almost Perfectly Handle Unnatural Scrambled Text
Qi Cao; Takeshi Kojima; Yutaka Matsuo; Yusuke Iwasawa;
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Highlight: In this study, we present novel experimental insights into the resilience of LLMs, particularly GPT-4, when subjected to extensive character-level permutations.
879, Exploring Linguistic Probes for Morphological Inflection
Jordan Kodner; Salam Khalifa; Sarah Ruth Brogden Payne;
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Highlight: In this paper, we supplement that approach with language-specific probes designed to test aspects of morphological generalization.
880, AMR Parsing with Causal Hierarchical Attention and Pointers
Chao Lou; Kewei Tu;
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Highlight: In this paper, we introduce new target forms of AMR parsing and a novel model, CHAP, which is equipped with causal hierarchical attention and the pointer mechanism, enabling the integration of structures into the Transformer decoder.
881, FLatS: Principled Out-of-Distribution Detection with Feature-Based Likelihood Ratio Score
Haowei Lin; Yuntian Gu;
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Highlight: We argue that the state-of-the-art (SOTA) feature-based OOD detection methods, such as Maha and KNN, are suboptimal since they only estimate in-distribution density pin(x). To address this issue, we propose FLATS, a principled solution for OOD detection based on likelihood ratio.
882, Fair Without Leveling Down: A New Intersectional Fairness Definition
Gaurav Maheshwari; Aur?lien Bellet; Pascal Denis; Mikaela Keller;
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Highlight: In this work, we consider the problem of intersectional group fairness in the classification setting, where the objective is to learn discrimination-free models in the presence of several intersecting sensitive groups.
883, CLAD-ST: Contrastive Learning with Adversarial Data for Robust Speech Translation
Sathish Indurthi; Shamil Chollampatt; Ravi Agrawal; Marco Turchi;
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Highlight: We address this robustness problem in downstream MT models by forcing the MT encoder to bring the representations of a noisy input closer to its clean version in the semantic space. This is achieved by introducing a contrastive learning method that leverages adversarial examples in the form of ASR outputs paired with their corresponding human transcripts to optimize the network parameters.
884, Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts
Siyuan Chen; Zhiling Zhang; Mengyue Wu; Kenny Zhu;
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Highlight: Many approaches are not backed by domain knowledge (e. g. , psychiatric symptoms) and thus fail to produce interpretable results. To tackle these issues, we propose an MDD framework that is capable of learning the shared clues of all diseases, while also capturing the specificity of each single disease.
885, Understanding The Role of Input Token Characters in Language Models: How Does Information Loss Affect Performance?
Ahmed Alajrami; Katerina Margatina; Nikolaos Aletras;
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Highlight: However, to the best of our knowledge, no previous work has specifically examined how information loss in input token characters affects the performance of PLMs. In this study, we address this gap by pre-training language models using small subsets of characters from individual tokens.
886, Improved Unsupervised Chinese Word Segmentation Using Pre-trained Knowledge and Pseudo-labeling Transfer
Hsiu-Wen Li; Ying-Jia Lin; Yi-Ting Li; Chun Lin; Hung-Yu Kao;
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Highlight: This work introduces a novel way to enhance UCWS performance while maintaining training efficiency.
887, EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs
Hanlin Tang; Yifu Sun; Decheng Wu; Kai Liu; Jianchen Zhu; Zhanhui Kang;
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Highlight: The problem is that in most previous works, the quantized model was calibrated using few samples from the training data, which might affect the generalization of the quantized LLMs to unknown cases and tasks. Hence in this work, we explore an important question: Can we design a data-independent quantization method for LLMs to guarantee its generalization performance?
888, Modeling Legal Reasoning: LM Annotation at The Edge of Human Agreement
Rosamond Thalken; Edward Stiglitz; David Mimno; Matthew Wilkens;
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Highlight: Our strongest results derive from fine-tuning models on the annotated dataset; the best performing model is an in-domain model, LEGAL-BERT. We apply predictions from this fine-tuned model to study historical trends in jurisprudence, an exercise that both aligns with prominent qualitative historical accounts and points to areas of possible refinement in those accounts.
889, Learning Co-Speech Gesture for Multimodal Aphasia Type Detection
Daeun Lee; Sejung Son; Hyolim Jeon; Seungbae Kim; Jinyoung Han;
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Highlight: Recognizing the importance of analyzing co-speech gestures for distinguish aphasia types, we propose a multimodal graph neural network for aphasia type detection using speech and corresponding gesture patterns.
890, Centering The Margins: Outlier-Based Identification of Harmed Populations in Toxicity Detection
Vyoma Raman; Eve Fleisig; Dan Klein;
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Highlight: The impact of AI models on marginalized communities has traditionally been measured by identifying performance differences between specified demographic subgroups. Though this approach aims to center vulnerable groups, it risks obscuring patterns of harm faced by intersectional subgroups or shared across multiple groups. To address this, we draw on theories of marginalization from disability studies and related disciplines, which state that people farther from the norm face greater adversity, to consider the ?margins? in the domain of toxicity detection.
891, Describe Me An Auklet: Generating Grounded Perceptual Category Descriptions
Bill Noble; Nikolai Ilinykh;
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Highlight: In this paper, we introduce a framework for testing category-level perceptual grounding in multi-modal language models.
892, We Need to Talk About Reproducibility in NLP Model Comparison
Yan Xue; Xuefei Cao; Xingli Yang; Yu Wang; Ruibo Wang; Jihong Li;
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Highlight: In this paper, we formulate the reproducibility in a model comparison into a probabilistic function with regard to a conclusion.
893, Practical Computational Power of Linear Transformers and Their Recurrent and Self-Referential Extensions
Kazuki Irie; R?bert Csord?s; J?rgen Schmidhuber;
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Highlight: Here we study auto-regressive Transformers with linearised attention, a. k. a. linear Transformers (LTs) or Fast Weight Programmers (FWPs).
894, Just Adjust One Prompt: Enhancing In-Context Dialogue Scoring Via Constructing The Optimal Subgraph of Demonstrations and Prompts
Jiashu Pu; Ling Cheng; Lu Fan; Tangjie Lv; Rongsheng Zhang;
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Highlight: To enable efficient and flexible adaptation to diverse needs of dialogue evaluation, we propose a dimension-agnostic scoring method that leverages the in-context learning (ICL) capability of LLMs to learn from human scoring to the fullest extent.
895, Multilingual Estimation of Political-party Positioning: From Label Aggregation to Long-input Transformers
Dmitry Nikolaev; Tanise Ceron; Sebastian Pad?;
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Highlight: In this work, we implement and compare two approaches to automatic scaling analysis of political-party manifestos: label aggregation, a pipeline strategy relying on annotations of individual statements from the manifestos, and long-input-Transformer-based models, which compute scaling values directly from raw text.
896, EpiK-Eval: Evaluation for Language Models As Epistemic Models
Gabriele Prato; Jerry Huang; Prasanna Parthasarathi; Shagun Sodhani; Sarath Chandar;
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Highlight: Despite their growing prevalence, their capacity to consolidate knowledge from different training documents?a crucial ability in numerous applications?remains unexplored. This paper presents the first study examining the capability of LLMs to effectively combine such information within their parameter space.
897, Statistical Depth for Ranking and Characterizing Transformer-Based Text Embeddings
Parker Seegmiller; Sarah Preum;
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Highlight: A statistical depth is a function for ranking k-dimensional objects by measuring centrality with respect to some observed k-dimensional distribution. We adopt a statistical depth to measure distributions of transformer-based text embeddings, transformer-based text embedding (TTE) depth, and introduce the practical use of this depth for both modeling and distributional inference in NLP pipelines.
898, Large Language Models Are Biased to Overestimate Profoundness
Eugenio Herrera-Berg; Tom?s Browne; Pablo Le?n-Villagr?; Marc-Llu?s Vives; Cristian Calderon;
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Highlight: We found a significant statement-to-statement correlation between the LLMs and humans, irrespective of the type of statements and the prompting technique used.
899, A Quality-based Syntactic Template Retriever for Syntactically-Controlled Paraphrase Generation
Xue Zhang; Songming Zhang; Yunlong Liang; Yufeng Chen; Jian Liu; Wenjuan Han; Jinan Xu;
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Highlight: For another, the templates automatically retrieved by current heuristic methods are usually unreliable for SPG models to generate qualified paraphrases. To escape this dilemma, we propose a novel Quality-based Syntactic Template Retriever (QSTR) to retrieve templates based on the quality of the to-be-generated paraphrases.
900, Active Learning for Natural Language Generation
Yotam Perlitz; Ariel Gera; Michal Shmueli-Scheuer; Dafna Sheinwald; Noam Slonim; Liat Ein-Dor;
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Highlight: In this paper, we present a first systematic study of active learning for NLG, considering a diverse set of tasks and multiple leading selection strategies, and harnessing a strong instruction-tuned model.
901, MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup
Hua Shen; Vicky Zayats; Johann Rocholl; Daniel Walker; Dirk Padfield;
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Highlight: However, numerous discontinuity phenomena in spoken conversational transcripts occur across multiple turns, which can not be identified by disfluency detection models. This study addresses these phenomena by proposing an innovative Multi-Turn Cleanup task for spoken conversational transcripts and collecting a new dataset, MultiTurnCleanup.
902, Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition
Srijith Radhakrishnan; Chao-Han Yang; Sumeer Khan; Rohit Kumar; Narsis Kiani; David Gomez-Cabrero; Jesper Tegn?r;
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Highlight: We introduce a new cross-modal fusion technique designed for generative error correction in automatic speech recognition (ASR).
903, Transformer-based Live Update Generation for Soccer Matches from Microblog Posts
Masashi Oshika; Kosuke Yamada; Ryohei Sasano; Koichi Takeda;
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Highlight: In this paper, we focus on soccer matches and work on building a system to generate live updates for soccer matches from tweets so that users can instantly grasp a match?s progress and enjoy the excitement of the match from raw tweets.
904, PromptST: Abstract Prompt Learning for End-to-End Speech Translation
Tengfei Yu; Liang Ding; Xuebo Liu; Kehai Chen; Meishan Zhang; Dacheng Tao; Min Zhang;
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Highlight: In this paper, we take the first step toward understanding the fusion of speech and text features in S2T model.
905, SAMRank: Unsupervised Keyphrase Extraction Using Self-Attention Map in BERT and GPT-2
Byungha Kang; Youhyun Shin;
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Highlight: We propose a novel unsupervised keyphrase extraction approach, called SAMRank, which uses only a self-attention map in a pre-trained language model (PLM) to determine the importance of phrases.
906, The Distributional Hypothesis Does Not Fully Explain The Benefits of Masked Language Model Pretraining
Ting-Rui Chiang; Dani Yogatama;
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Highlight: We analyze the masked language modeling pretraining objective function from the perspective of the Distributional Hypothesis.
907, Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs
Yatin Nandwani; Vineet Kumar; Dinesh Raghu; Sachindra Joshi; Luis Lastras;
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Highlight: However, these automated metrics are far from being well aligned with human judgments. Therefore, to improve the measurement of faithfulness, we propose a new metric that utilizes (Conditional) Point-wise Mutual Information (PMI) between the generated response and the source document, conditioned on the dialogue.
908, Using Artificial French Data to Understand The Emergence of Gender Bias in Transformer Language Models
Lina Conti; Guillaume Wisniewski;
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Highlight: We propose to use an artificial corpus generated by a PCFG based on French to precisely control the gender distribution in the training data and determine under which conditions a model correctly captures gender information or, on the contrary, appears gender-biased.
909, Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification
Mujeen Sung; James Gung; Elman Mansimov; Nikolaos Pappas; Raphael Shu; Salvatore Romeo; Yi Zhang; Vittorio Castelli;
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Highlight: We propose a novel pre-training method for text encoders that uses contrastive learning with intent psuedo-labels to produce embeddings that are well-suited for IC tasks, reducing the need for manual annotations.
910, GazeVQA: A Video Question Answering Dataset for Multiview Eye-Gaze Task-Oriented Collaborations
Muhammet Ilaslan; Chenan Song; Joya Chen; Difei Gao; Weixian Lei; Qianli Xu; Joo Lim; Mike Shou;
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Highlight: In this paper, we build a novel task-oriented VQA dataset, called GazeVQA, for collaborative tasks where gaze information is captured during the task process.
911, Balance Act: Mitigating Hubness in Cross-Modal Retrieval with Query and Gallery Banks
Yimu Wang; Xiangru Jian; Bo Xue;
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Highlight: In this work, we present a post-processing solution to address the hubness problem in cross-modal retrieval, a phenomenon where a small number of gallery data points are frequently retrieved, resulting in a decline in retrieval performance.
912, What Do Deck Chairs and Sun Hats Have in Common? Uncovering Shared Properties in Large Concept Vocabularies
Amit Gajbhiye; Zied Bouraoui; Na Li; Usashi Chatterjee; Luis Espinosa-Anke; Steven Schockaert;
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Highlight: But concepts can be modelled from different perspectives, whereas concept embeddings typically mostly capture taxonomic structure. To address this issue, we propose a strategy for identifying what different concepts, from a potentially large concept vocabulary, have in common with others.
913, Bridging The Digital Divide: Performance Variation Across Socio-Economic Factors in Vision-Language Models
Joan Nwatu; Oana Ignat; Rada Mihalcea;
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Highlight: We evaluate the performance of a state-of-the-art vision-language model (CLIP) on a geo-diverse dataset containing household images associated with different income values (DollarStreet) and show that performance inequality exists among households of different income levels.
914, AMR Parsing Is Far from Solved: GrAPES, The Granular AMR Parsing Evaluation Suite
Jonas Groschwitz; Shay Cohen; Lucia Donatelli; Meaghan Fowlie;
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Highlight: We present the Granular AMR Parsing Evaluation Suite (GrAPES), a challenge set for Abstract Meaning Representation (AMR) parsing with accompanying evaluation metrics.
915, AD-NLP: A Benchmark for Anomaly Detection in Natural Language Processing
Matei Bejan; Andrei Manolache; Marius Popescu;
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Highlight: In the present work, we provide a unified benchmark for detecting various types of anomalies, focusing on problems that can be naturally formulated as Anomaly Detection in text, ranging from syntax to stylistics.
916, Enhancing The Ranking Context of Dense Retrieval Through Reciprocal Nearest Neighbors
George Zerveas; Navid Rekabsaz; Carsten Eickhoff;
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Highlight: Sparse annotation poses persistent challenges to training dense retrieval models; for example, it distorts the training signal when unlabeled relevant documents are used spuriously as negatives in contrastive learning. To alleviate this problem, we introduce evidence-based label smoothing, a novel, computationally efficient method that prevents penalizing the model for assigning high relevance to false negatives.
917, Cross-Lingual Cross-Target Stance Detection with Dual Knowledge Distillation Framework
Ruike Zhang; Hanxuan Yang; Wenji Mao;
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Highlight: Moreover, target inconsistency in cross-lingual stance detection brings about the additional issue of unseen targets in target language, which in essence requires the transfer of both language and target-oriented knowledge from source to target language. To tackle these challenging issues, in this paper, we propose the new task of cross-lingual cross-target stance detection and develop the first computational work with dual knowledge distillation.
918, An Iteratively Parallel Generation Method with The Pre-Filling Strategy for Document-level Event Extraction
Guanhua Huang; Runxin Xu; Ying Zeng; Jiaze Chen; Zhouwang Yang; Weinan E;
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Highlight: In this paper, we propose an Iteratively Parallel Generation method with the Pre-Filling strategy (IPGPF).
919, FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization
Nan Zhang; Yusen Zhang; Wu Guo; Prasenjit Mitra; Rui Zhang;
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Highlight: In this paper, we investigate and improve faithfulness in summarization on a broad range of medical summarization tasks.
920, Systematic Word Meta-sense Extension
Lei Yu;
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Highlight: We propose a novel analogy-based method of word meaning extension, and show that it effectively improves language model systematicity in making both gradual and radical types of meta-sense extension.
921, Revisiting The Knowledge Injection Frameworks
Peng Fu; Yiming Zhang; Haobo Wang; Weikang Qiu; Junbo Zhao;
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Highlight: Simply put, we find that injecting unaligned (i. e. , random) knowledge tuple into the LLMs achieves comparable (and sometimes better) results than the aligned knowledge being injected. We therefore take a thorough investigation of this frustrating finding on a variety of related prior work and further provide a chain of potential interpretations for the phenomenon.
922, We Are What We Repeatedly Do: Inducing and Deploying Habitual Schemas in Persona-Based Responses
Benjamin Kane; Lenhart Schubert;
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Highlight: However, in realistic human conversations, personas are often revealed through story-like narratives that involve rich habitual knowledge ? knowledge about kinds of events that an agent often participates in (e. g. , work activities, hobbies, sporting activities, favorite entertainments, etc. ), including typical goals, sub-events, preconditions, and postconditions of those events. We capture such habitual knowledge using an explicit schema representation, and propose an approach to dialogue generation that retrieves relevant schemas to condition a large language model to generate persona-based responses.
923, Zero-shot Faithfulness Evaluation for Text Summarization with Foundation Language Model
Qi Jia; Siyu Ren; Yizhu Liu; Kenny Zhu;
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Highlight: This paper proposes to do zero-shot faithfulness evaluation simply with a moderately-sized foundation language model.
924, Improving Bias Mitigation Through Bias Experts in Natural Language Understanding
Eojin Jeon; Mingyu Lee; Juhyeong Park; Yeachan Kim; Wing-Lam Mok; SangKeun Lee;
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Highlight: As an alternative, we propose a new debiasing framework that introduces binary classifiers between the auxiliary model and the main model, coined bias experts.
925, Semi-supervised Multimodal Coreference Resolution in Image Narrations
Arushi Goel; Basura Fernando; Frank Keller; Hakan Bilen;
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Highlight: In this paper, we study multimodal coreference resolution, specifically where a longer descriptive text, i. e. , a narration is paired with an image.
926, Argument-based Detection and Classification of Fallacies in Political Debates
Pierpaolo Goffredo; Mariana Espinoza; Serena Villata; Elena Cabrio;
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Highlight: Our contribution to address this challenging task is twofold. First, we extend the ElecDeb60To16 dataset of U. S. presidential debates annotated with fallacious arguments, by incorporating the most recent Trump-Biden presidential debate. We include updated token-level annotations, incorporating argumentative components (i. e. , claims and premises), the relations between these components (i. e. , support and attack), and six categories of fallacious arguments (i. e. , Ad Hominem, Appeal to Authority, Appeal to Emotion, False Cause, Slippery Slope, and Slogans). Second, we perform the twofold task of fallacious argument detection and classification by defining neural network architectures based on Transformers models, combining text, argumentative features, and engineered features.
927, SpEL: Structured Prediction for Entity Linking
Hassan Shavarani; Anoop Sarkar;
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Highlight: Our system, called SpEL (Structured prediction for Entity Linking) is a state-of-the-art entity linking system that uses some new ideas to apply structured prediction to the task of entity linking including: two refined fine-tuning steps; a context sensitive prediction aggregation strategy; reduction of the size of the model?s output vocabulary, and; we address a common problem in entity-linking systems where there is a training vs. inference tokenization mismatch.
928, Architectural Sweet Spots for Modeling Human Label Variation By The Example of Argument Quality: It?s Best to Relate Perspectives!
Philipp Heinisch; Matthias Orlikowski; Julia Romberg; Philipp Cimiano;
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Highlight: Our findings indicate that approaches to subjectivity can benefit from relating individual perspectives.
929, Polyglot or Not? Measuring Multilingual Encyclopedic Knowledge in Foundation Models
Tim Schott; Daniel Furman; Shreshta Bhat;
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Highlight: In this work, we assess the ability of foundation models to recall encyclopedic knowledge across a wide range of linguistic contexts.
930, Anchoring Fine-tuning of Sentence Transformer with Semantic Label Information for Efficient Truly Few-shot Classification
Amalie Pauli; Leon Derczynski; Ira Assent;
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Highlight: We propose an efficient method with small model sizes and less training data with only 2-8 training instances per class.
931, TATA: Stance Detection Via Topic-Agnostic and Topic-Aware Embeddings
Hans Hanley; Zakir Durumeric;
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Highlight: In this work, we propose using contrastive learning as well as an unlabeled dataset of news articles that cover a variety of different topics to train topic-agnostic/TAG and topic-aware/TAW embeddings for use in downstream stance detection.
932, Data Similarity Is Not Enough to Explain Language Model Performance
Gregory Yauney; Emily Reif; David Mimno;
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Highlight: We test whether distributional and example-specific similarity measures (embedding-, token- and model-based) correlate with language model performance through a large-scale comparison of the Pile and C4 pretraining datasets with downstream benchmarks.
933, An ?Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives?
Young Cho; Sunny Rai; Lyle Ungar; Jo?o Sedoc; Sharath Guntuku;
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Highlight: Based on our findings on transparency, ethics, and cultural heterogeneity in this review, we provide a few recommendations to help bridge the disciplinary divide and enable the cross-disciplinary development of mental health conversational agents.
934, Axiomatic Preference Modeling for Longform Question Answering
Corby Rosset; Guoqing Zheng; Victor Dibia; Ahmed Awadallah; Paul Bennett;
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Highlight: The contributions of this work include: training a standalone preference model that can score human- and LLM-generated answers on the same scale; developing an axiomatic framework for generating training data pairs tailored to certain principles; and showing that a small amount of axiomatic signals can help small models outperform GPT-4 in preference scoring.
935, Seq2seq Is All You Need for Coreference Resolution
Wenzheng Zhang; Sam Wiseman; Karl Stratos;
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Highlight: Existing works on coreference resolution suggest that task-specific models are necessary to achieve state-of-the-art performance. In this work, we present compelling evidence that such models are not necessary.
936, Integrating Language Models Into Direct Speech Translation: An Inference-Time Solution to Control Gender Inflection
Dennis Fucci; Marco Gaido; Sara Papi; Mauro Cettolo; Matteo Negri; Luisa Bentivogli;
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Highlight: The existing solutions to do so, though effective, are hardly feasible in practice as they involve dedicated model re-training on gender-labeled ST data. To overcome these limitations, we propose the first inference-time solution to control speaker-related gender inflections in ST. Our approach partially replaces the (biased) internal language model (LM) implicitly learned by the ST decoder with gender-specific external LMs.
937, StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding
Cheng Jiayang; Lin Qiu; Tsz Chan; Tianqing Fang; Weiqi Wang; Chunkit Chan; Dongyu Ru; Qipeng Guo; Hongming Zhang; Yangqiu Song; Yue Zhang; Zheng Zhang;
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Highlight: In this paper, we evaluate the ability to identify and generate analogies by constructing a first-of-its-kind large-scale story-level analogy corpus, StoryAnalogy, which contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory.
938, Beyond Detection: A Defend-and-Summarize Strategy for Robust and Interpretable Rumor Analysis on Social Media
Yi-Ting Chang; Yun-Zhu Song; Yi-Syuan Chen; Hong-Han Shuai;
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Highlight: Moreover, existing detection models have poor interpretability. To address these issues, we propose a novel framework named **D**efend-**A**nd-**S**ummarize (DAS) based on the concept that responses sharing similar opinions should exhibit similar features.
939, Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors
Nikita Mehandru; Sweta Agrawal; Yimin Xiao; Ge Gao; Elaine Khoong; Marine Carpuat; Niloufar Salehi;
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Highlight: Using Emergency Department discharge instructions, we study how interventions based on quality estimation versus backtranslation assist physicians in deciding whether to show MT outputs to a patient.
940, Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What Is Offensive
Tharindu Weerasooriya; Sujan Dutta; Tharindu Ranasinghe; Marcos Zampieri; Christopher Homan; Ashiqur KhudaBukhsh;
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Highlight: This paper investigates how machine and human moderators disagree on what is offensive when it comes to real-world social web political discourse.
941, Generating Summaries with Controllable Readability Levels
Leonardo Ribeiro; Mohit Bansal; Markus Dreyer;
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Highlight: However, current text generation approaches lack refined control, resulting in texts that are not customized to readers? proficiency levels. In this work, we bridge this gap and study techniques to generate summaries at specified readability levels.
942, CodeFusion: A Pre-trained Diffusion Model for Code Generation
Mukul Singh; Jos? Cambronero; Sumit Gulwani; Vu Le; Carina Negreanu; Gust Verbruggen;
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Highlight: Auto-regressive models for code generation from natural language have a similar limitation: they do not easily allow reconsidering earlier tokens generated. We introduce CodeFusion, a pre-trained diffusion code generation model that addresses this limitation by iteratively denoising a complete program conditioned on the encoded natural language.
943, CESAR: Automatic Induction of Compositional Instructions for Multi-turn Dialogs
Taha Aksu; Devamanyu Hazarika; Shikib Mehri; Seokhwan Kim; Dilek Hakkani-Tur; Yang Liu; Mahdi Namazifar;
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Highlight: While publicly available LLMs have shown promising performance, when exposed to complex instructions with multiple constraints, they lag against state-of-the-art models like ChatGPT. In this work, we hypothesize that the availability of large-scale complex demonstrations is crucial in bridging this gap.
944, From Parse-Execute to Parse-Execute-Refine: Improving Semantic Parser for Complex Question Answering Over Knowledge Base
Wangzhen Guo; Linyin Luo; Hanjiang Lai; Jian Yin;
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Highlight: Specifically, we propose three components: a parsing stage, an execution stage and a refinement stage, to enhance the ability of complex reasoning.
945, Cabbage Sweeter Than Cake? Analysing The Potential of Large Language Models for Learning Conceptual Spaces
Usashi Chatterjee; Amit Gajbhiye; Steven Schockaert;
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Highlight: These quality dimensions are usually learned from human judgements, which means that applications of conceptual spaces tend to be limited to narrow domains (e. g. modelling colour or taste). Encouraged by recent findings about the ability of Large Language Models (LLMs) to learn perceptually grounded representations, we explore the potential of such models for learning conceptual spaces.
946, Once Upon A Time in Graph: Relative-Time Pretraining for Complex Temporal Reasoning
Sen Yang; Xin Li; Lidong Bing; Wai Lam;
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Highlight: In this work, we make use of the underlying nature of time, all temporally-scoped sentences are strung together through a one-dimensional time axis, and suggest creating a graph structure based on the relative placements of events along the time axis.
947, Expository Text Generation: Imitate, Retrieve, Paraphrase
Nishant Balepur; Jie Huang; Kevin Chang;
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Highlight: Despite their usefulness, writing expository text by hand is a challenging process that requires careful content planning, obtaining facts from multiple sources, and the ability to clearly synthesize these facts. To ease these burdens, we propose the task of expository text generation, which seeks to automatically generate an accurate and stylistically consistent expository text for a topic by intelligently searching a knowledge source.
948, Large-scale Similarity Search with Optimal Transport
Cl?a Laouar; Yuki Takezawa; Makoto Yamada;
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Highlight: In this study, we propose a simple and effective nearest neighbor search based on the Wasserstein distance.
949, Continual Event Extraction with Semantic Confusion Rectification
Zitao Wang; Xinyi Wang; Wei Hu;
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Highlight: This paper proposes a novel continual event extraction model with semantic confusion rectification.
950, An Empirical Study of Translation Hypothesis Ensembling with Large Language Models
Ant?nio Farinhas; Jos? de Souza; Andre Martins;
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Highlight: In this paper, we investigate how hypothesis ensembling can improve the quality of the generated text for the specific problem of LLM-based machine translation.
951, FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones Via Federated Learning
Jaemin Shin; Hyungjun Yoon; Seungjoo Lee; Sungjoon Park; Yunxin Liu; Jinho Choi; Sung-Ju Lee;
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Highlight: We propose FedTherapist, a mobile mental health monitoring system that utilizes continuous speech and keyboard input in a privacy-preserving way via federated learning.
952, Continual Learning for Multilingual Neural Machine Translation Via Dual Importance-based Model Division
Junpeng Liu; Kaiyu Huang; Hao Yu; Jiuyi Li; Jinsong Su; Degen Huang;
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Highlight: To achieve this, the existing methods primarily focus on preventing catastrophic forgetting by making compromises between the original and new language pairs, leading to sub-optimal performance on both translation tasks. To mitigate this problem, we propose a dual importance-based model division method to divide the model parameters into two parts and separately model the translation of the original and new tasks.
953, Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought Through Interaction with Symbolic Systems
Marek Kadlc?k; Michal ?tef?nik; Ondrej Sotolar; Vlastimil Martinek;
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Highlight: Despite outstanding performance in many tasks, language models are notoriously inclined to make factual errors in tasks requiring arithmetic computation. We address this deficiency by creating Calc-X, a collection of datasets that demonstrates the appropriate use of a calculator in reasoning chains.
954, Emergence of Abstract State Representations in Embodied Sequence Modeling
Tian Yun; Zilai Zeng; Kunal Handa; Ashish Thapliyal; Bo Pang; Ellie Pavlick; Chen Sun;
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Highlight: A model that lacks abstract state representations would be liable to make decisions based on surface statistics which fail to generalize. We take the BabyAI environment, a grid world in which language-conditioned navigation tasks are performed, and build a sequence modeling Transformer, which takes a language instruction, a sequence of actions, and environmental observations as its inputs.
955, StereoMap: Quantifying The Awareness of Human-like Stereotypes in Large Language Models
Sullam Jeoung; Yubin Ge; Jana Diesner;
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Highlight: We propose a theoretically grounded framework called StereoMap to gain insights into their perceptions of how demographic groups have been viewed by society.
956, Select, Prompt, Filter: Distilling Large Language Models for Summarizing Conversations
Minh-Quang Pham; Sathish Indurthi; Shamil Chollampatt; Marco Turchi;
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Highlight: We propose a three-step approach to distill ChatGPT and fine-tune smaller LMs for summarizing forum conversations.
957, Human Raters Cannot Distinguish English Translations from Original English Texts
Shira Wein;
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Highlight: In this work, we perform a human evaluation of English original/translated texts in order to explore raters? ability to classify texts as being original or translated English and the features that lead a rater to judge text as being translated.
958, Text Embeddings Reveal (Almost) As Much As Text
John Morris; Volodymyr Kuleshov; Vitaly Shmatikov; Alexander Rush;
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Highlight: We investigate the problem of embedding inversion, reconstructing the full text represented in dense text embeddings.
959, Hallucination Mitigation in Natural Language Generation from Large-Scale Open-Domain Knowledge Graphs
Xiao Shi; Zhengyuan Zhu; Zeyu Zhang; Chengkai Li;
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Highlight: We propose a novel approach that, given a graph-sentence pair in GraphNarrative, trims the sentence to eliminate portions that are not present in the corresponding graph, by utilizing the sentence?s dependency parse tree.
960, Identifying Statements Crucial for Awareness of Interpretive Nonsense to Prevent Communication Breakdowns
Tomoyuki Maekawa; Michita Imai;
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Highlight: During remote conversations, communication breakdowns often occur when a listener misses certain statements. Our objective is to prevent such breakdowns by identifying Statements Crucial for Awareness of Interpretive Nonsense (SCAINs).
961, Reinforced Target-driven Conversational Promotion
Huy Dao; Lizi Liao; Dung Le; Yuxiang Nie;
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Highlight: In this work, we propose a Reinforced Target-driven Conversational Promotion (RTCP) framework for conversational promotion.
962, Identification of Multimodal Stance Towards Frames of Communication
Maxwell Weinzierl; Sanda Harabagiu;
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Highlight: In this paper we introduce MMVax-Stance, a dataset of 11,300 multimedia documents retrieved from social media, which have stance annotations towards 113 different frames of communication.
963, Large Language Models: The Need for Nuance in Current Debates and A Pragmatic Perspective on Understanding
Bram van Dijk; Tom Kouwenhoven; Marco Spruit; Max Johannes van Duijn;
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Highlight: LLMs are appearing rapidly, and debates on LLM capacities have taken off, but reflection is lagging behind. Thus, in this position paper, we first zoom in on the debate and critically assess three points recurring in critiques of LLM capacities: i) that LLMs only parrot statistical patterns in the training data; ii) that LLMs master formal but not functional language competence; and iii) that language learning in LLMs cannot inform human language learning.
964, Predictive Chemistry Augmented with Text Retrieval
Yujie Qian; Zhening Li; Zhengkai Tu; Connor Coley; Regina Barzilay;
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Highlight: In this paper, we introduce TextReact, a novel method that directly augments predictive chemistry with texts retrieved from the literature.
965, System Combination Via Quality Estimation for Grammatical Error Correction
Muhammad Reza Qorib; Hwee Tou Ng;
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Highlight: In this paper, we propose GRECO, a new state-of-the-art quality estimation model that gives a better estimate of the quality of a corrected sentence, as indicated by having a higher correlation to the F0.
966, Joint Geometrical and Statistical Domain Adaptation for Cross-domain Code Vulnerability Detection
Qianjin Du; Shiji Zhou; Xiaohui Kuang; Gang Zhao; Jidong Zhai;
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Highlight: In addition, these methods forcibly reduce the distribution discrepancy between domains and do not take into account the interference of irrelevant target instances for distributional domain alignment, which leads to the problem of excessive alignment. To address the above issues, we propose a novel cross-domain code vulnerability detection framework named MNCRI.
967, CLEVR-Implicit: A Diagnostic Dataset for Implicit Reasoning in Referring Expression Comprehension
Jingwei Zhang; Xin Wu; Yi Cai;
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Highlight: To address the challenge, we introduce CLEVR-Implicit, a dataset consisting of synthetic images and corresponding two types of implicit text for the REC task.
968, A Deeper (Autoregressive) Approach to Non-Convergent Discourse Parsing
Oren Tsur; Yoav Tulpan;
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Highlight: In this work, we present a unified model for Non-Convergent Discourse Parsing that does not require any additional input other than the previous dialog utterances.
969, Multi-view Contrastive Learning for Entity Typing Over Knowledge Graphs
Zhiwei Hu; Victor Basulto; Zhiliang Xiang; Ru Li; Jeff Pan;
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Highlight: In this paper, we propose a novel method called Multi-view Contrastive Learning for knowledge graph Entity Typing MCLET, which effectively encodes the coarse-grained knowledge provided by clusters into entity and type embeddings.
970, Optimized Tokenization for Transcribed Error Correction
Tomer Wullach; Shlomo Chazan;
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Highlight: In this paper, we demonstrate that the performance of correction models can be significantly increased by training solely using synthetic data.
971, Beware of Model Collapse! Fast and Stable Test-time Adaptation for Robust Question Answering
Yi Su; Yixin Ji; Juntao Li; Hai Ye; Min Zhang;
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Highlight: In this paper, we delve into why TTA causes model collapse and find that the imbalanced label distribution inherent in QA is the reason for it.
972, Generative Adversarial Training with Perturbed Token Detection for Model Robustness
Jiahao Zhao; Wenji Mao;
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Highlight: Moreover, the continuous representations of perturbations cannot be further utilized, resulting in the suboptimal performance. To bridge this gap for adversarial robustness, in this paper, we devise a novel generative adversarial training framework that integrates gradient-based learning, adversarial example generation and perturbed token detection.
973, Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation
Anastasia Kritharoula; Maria Lymperaiou; Giorgos Stamou;
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Highlight: Visual Word Sense Disambiguation (VWSD) is a novel challenging task with the goal of retrieving an image among a set of candidates, which better represents the meaning of an ambiguous word within a given context. In this paper, we make a substantial step towards unveiling this interesting task by applying a varying set of approaches.
974, Be Selfish, But Wisely: Investigating The Impact of Agent Personality in Mixed-Motive Human-Agent Interactions
Kushal Chawla; Ian Wu; Yu Rong; Gale Lucas; Jonathan Gratch;
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Highlight: Grounded in negotiation theory from Economics, we modify the training procedure in two novel ways to design agents with diverse personalities and analyze their performance with human partners.
975, On Evaluation of Bangla Word Analogies
Mousumi Akter; Souvika Sarkar; Shubhra Kanti Karmaker Santu;
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Highlight: This paper presents a benchmark dataset of Bangla word analogies for evaluating the quality of existing Bangla word embeddings.
976, Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts
Haochen Tan; Han Wu; Wei Shao; Xinyun Zhang; Mingjie Zhan; Zhaohui Hou; Ding Liang; Linqi Song;
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Highlight: Meetings typically involve multiple participants and lengthy conversations, resulting in redundant and trivial content. To overcome these challenges, we propose a two-step framework, Reconstruct before Summarize (RbS), for effective and efficient meeting summarization.
977, Natural Language Decompositions of Implicit Content Enable Better Text Representations
Alexander Hoyle; Rupak Sarkar; Pranav Goel; Philip Resnik;
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Highlight: When people interpret text, they rely on inferences that go beyond the observed language itself. Inspired by this observation, we introduce a method for the analysis of text that takes implicitly communicated content explicitly into account.
978, A Scalable Framework for Table of Contents Extraction from Complex ESG Annual Reports
Xinyu Wang; Lin Gui; Yulan He;
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Highlight: In this paper, we propose a new dataset, ESGDoc, comprising 1,093 ESG annual reports from 563 companies spanning from 2001 to 2022.
979, Semantic Space Grounded Weighted Decoding for Multi-Attribute Controllable Dialogue Generation
Zhiling Zhang; Mengyue Wu; Kenny Zhu;
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Highlight: We propose a novel framework called DASC that possesses strong controllability with a weighted decoding paradigm, while improving generation quality with the grounding in an attribute semantics space.
980, NameGuess: Column Name Expansion for Tabular Data
Jiani Zhang; Zhengyuan Shen; Balasubramaniam Srinivasan; Shen Wang; Huzefa Rangwala; George Karypis;
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Highlight: One common challenge when dealing with large volumes of tabular data is the pervasive use of abbreviated column names, which can negatively impact performance on various data search, access, and understanding tasks. To address this issue, we introduce a new task, called NameGuess, to expand column names (used in database schema) as a natural language generation problem.
981, BLESS: Benchmarking Large Language Models on Sentence Simplification
Tannon Kew; Alison Chi; Laura V?squez-Rodr?guez; Sweta Agrawal; Dennis Aumiller; Fernando Alva-Manchego; Matthew Shardlow;
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Highlight: We present BLESS, a comprehensive performance benchmark of the most recent state-of-the-art Large Language Models (LLMs) on the task of text simplification (TS).
982, An Exploration of Left-Corner Transformations
Andreas Opedal; Eleftheria Tsipidi; Tiago Pimentel; Ryan Cotterell; Tim Vieira;
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Highlight: This paper generalizes prior left-corner transformations to support semiring-weighted production rules and to provide finer-grained control over which left corners may be moved.
983, Characterizing and Verifying Scientific Claims: Qualitative Causal Structure Is All You Need
Jinxuan Wu; Wenhan Chao; Xian Zhou; Zhunchen Luo;
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Highlight: We organize the qualitative causal structure into a heterogeneous graph and propose a novel attention-based graph neural network model to facilitate causal reasoning across relevant causally-potent factors.
984, ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed As Text Games
Ruoyao Wang; Graham Todd; Xingdi Yuan; Ziang Xiao; Marc-Alexandre C?t?; Peter Jansen;
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Highlight: In this work we investigate the capacity of language models to generate explicit, interpretable, and interactive world models of scientific and common-sense reasoning tasks.
985, MaNtLE: Model-agnostic Natural Language Explainer
Rakesh Menon; Kerem Zaman; Shashank Srivastava;
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Highlight: In this paper, we introduce MaNtLE, a model-agnostic natural language explainer that analyzes a set of classifier predictions and generates faithful natural language explanations of classifier rationale for structured classification tasks.
986, Ling-CL: Understanding NLP Models Through Linguistic Curricula
Mohamed Elgaar; Hadi Amiri;
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Highlight: We employ a characterization of linguistic complexity from psycholinguistic and language acquisition research to develop data-driven curricula to understand the underlying linguistic knowledge that models learn to address NLP tasks.
987, Towards A Better Understanding of Variations in Zero-Shot Neural Machine Translation Performance
Shaomu Tan; Christof Monz;
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Highlight: Through systematic experimentation, spanning 1,560 language directions across 40 languages, we identify three key factors contributing to high variations in ZS NMT performance: 1) target-side translation quality, 2) vocabulary overlap, and 3) linguistic properties.
988, SEER : A Knapsack Approach to Exemplar Selection for In-Context HybridQA
Jonathan Tonglet; Manon Reusens; Philipp Borchert; Bart Baesens;
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Highlight: In this work, we present Selection of ExEmplars for hybrid Reasoning (SEER), a novel method for selecting a set of exemplars that is both representative and diverse.
989, Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations
Jihyoung Jang; Minseong Boo; Hyounghun Kim;
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Highlight: In this paper, we introduce a new 1M multi-session dialogue dataset, called Conversation Chronicles, for implementing a long-term conversation setup in which time intervals and fine-grained speaker relationships are incorporated.
990, MoPe: Model Perturbation Based Privacy Attacks on Language Models
Marvin Li; Jason Wang; Jeffrey Wang; Seth Neel;
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Highlight: In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training data of a pre-trained language model, given white-box access to the models parameters.
991, You Told Me That Joke Twice: A Systematic Investigation of Transferability and Robustness of Humor Detection Models
Alexander Baranov; Vladimir Kniazhevsky; Pavel Braslavski;
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Highlight: In this study, we focus on automatic humor detection, a highly relevant task for conversational AI.
992, Unifying Discrete and Continuous Representations for Unsupervised Paraphrase Generation
Mingfeng Xue; Dayiheng Liu; Wenqiang Lei; Jie Fu; Jian Lan; Mei Li; Baosong Yang; Jun Xie; Yidan Zhang; Dezhong Peng; Jiancheng Lv;
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Highlight: To obviate the reliance on translation data and prompt greater variations in surface structure, we propose a self-supervised pseudo-data construction method that generates diverse pseudo-paraphrases in distinct surface structures for a given sentence.
993, Finding Authentic Counterhate Arguments: A Case Study with Public Figures
Abdullah Albanyan; Ahmed Hassan; Eduardo Blanco;
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Highlight: Thus, we present a corpus of 54,816 hateful tweet-paragraph pairs, where the paragraphs are candidate counterhate arguments.
994, AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
Shamsuddeen Muhammad; Idris Abdulmumin; Abinew Ayele; Nedjma Ousidhoum; David Adelani; Seid Yimam; Ibrahim Ahmad; Meriem Beloucif; Saif Mohammad; Sebastian Ruder; Oumaima Hourrane; Alipio Jorge; Pavel Brazdil; Felermino Ali; Davis David; Salomey Osei; Bello Shehu-Bello; Falalu Lawan; Tajuddeen Gwadabe; Samuel Rutunda; Tadesse Belay; Wendimu Messelle; Hailu Balcha; Sisay Chala; Hagos Gebremichael; Bernard Opoku; Stephen Arthur;
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Highlight: In this paper, we introduce AfriSenti, a sentiment analysis benchmark that contains a total of >110,000 tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba) from four language families.
995, Syllogistic Reasoning for Legal Judgment Analysis
Wentao Deng; Jiahuan Pei; Keyi Kong; Zhe Chen; Furu Wei; Yujun Li; Zhaochun Ren; Zhumin Chen; Pengjie Ren;
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Highlight: In this paper, we construct and manually correct a syllogistic reasoning dataset for legal judgment analysis.
996, Improving Transformer-based Program Repair Model Through False Behavior Diagnosis
Youngkyoung Kim; Misoo Kim; Eunseok Lee;
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Highlight: Thus, we propose a methodology for diagnosing and treating the false behaviors of transformer-based program repair models.
997, This Reads Like That: Deep Learning for Interpretable Natural Language Processing
Claudio Fanconi; Moritz Vandenhirtz; Severin Husmann; Julia Vogt;
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Highlight: We introduce a learned weighted similarity measure that enhances the similarity computation by focusing on informative dimensions of pre-trained sentence embeddings.
998, Addressing NER Annotation Noises with Uncertainty-Guided Tree-Structured CRFs
Jian Liu; Weichang Liu; Yufeng Chen; Jinan Xu; Zhe Zhao;
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Highlight: In this paper, we present a new and unified approach to tackle annotation noises for NER.
999, Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation
Jason Lucas; Adaku Uchendu; Michiharu Yamashita; Jooyoung Lee; Shaurya Rohatgi; Dongwon Lee;
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Highlight: i. e, generating large-scale harmful and misleading content*). To combat this emerging risk of LLMs, we propose a novel ?***Fighting Fire with Fire***? (F3) strategy that harnesses modern LLMs? generative and emergent reasoning capabilities to counter human-written and LLM-generated disinformation.
1000, SMoP: Towards Efficient and Effective Prompt Tuning with Sparse Mixture-of-Prompts
Joon-Young Choi; Junho Kim; Jun-Hyung Park; Wing-Lam Mok; SangKeun Lee;
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Highlight: In this paper, we propose a novel prompt tuning method SMoP (Sparse Mixture-of-Prompts) that utilizes short soft prompts for efficient training and inference while maintaining performance gains typically induced from longer soft prompts.
1001, When Are Lemons Purple? The Concept Association Bias of Vision-Language Models
Yingtian Tang; Yutaro Yamada; Yoyo Zhang; Ilker Yildirim;
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Highlight: However, such performance does not realize in tasks that require a finer-grained correspondence between vision and language, such as Visual Question Answering (VQA). We investigate why this is the case, and report an interesting phenomenon of vision-language models, which we call the Concept Association Bias (CAB), as a potential cause of the difficulty of applying these models to VQA and similar tasks.
1002, Text Representation Distillation Via Information Bottleneck Principle
Yanzhao Zhang; Dingkun Long; Zehan Li; Pengjun Xie;
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Highlight: In order to relieve the issue of performance degradation after distillation, we propose a novel Knowledge Distillation method called IBKD.
1003, Exploring The Boundaries of GPT-4 in Radiology
Qianchu Liu; Stephanie Hyland; Shruthi Bannur; Kenza Bouzid; Daniel Castro; Maria Wetscherek; Robert Tinn; Harshita Sharma; Fernando P?rez-Garc?a; Anton Schwaighofer; Pranav Rajpurkar; Sameer Khanna; Hoifung Poon; Naoto Usuyama; Anja Thieme; Aditya Nori; Matthew Lungren; Ozan Oktay; Javier Alvarez-Valle;
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Highlight: In this paper, we focus on assessing the performance of GPT-4, the most capable LLM so far, on the text-based applications for radiology reports, comparing against state-of-the-art (SOTA) radiology-specific models.
1004, A Frustratingly Easy Post-Training Quantization Scheme for LLMs
Yongkweon Jeon; Chungman Lee; Kyungphil Park; Ho-young Kim;
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Highlight: In this paper, we propose a straightforward post-training quantization scheme, called Z-Fold, that fully utilizes the feature of the Transformer structure widely employed in large language models.
1005, A Comprehensive Evaluation of Biomedical Entity Linking Models
David Kartchner; Jennifer Deng; Shubham Lohiya; Tejasri Kopparthi; Prasanth Bathala; Daniel Domingo-Fern?ndez; Cassie Mitchell;
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Highlight: The study objective was to comprehensively evaluate nine recent state-of-the-art biomedical entity linking models under a unified framework. We compare these models along axes of (1) accuracy, (2) speed, (3) ease of use, (4) generalization, and (5) adaptability to new ontologies and datasets.
1006, LIMIT: Language Identification, Misidentification, and Translation Using Hierarchical Models in 350+ Languages
Milind Agarwal; Md Mahfuz Ibn Alam; Antonios Anastasopoulos;
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Highlight: Language identification is a well-studied problem, sometimes even considered solved; in reality, due to lack of data and computational challenges, current systems cannot accurately identify most of the world?s 7000 languages. To tackle this bottleneck, we first compile a corpus, MCS-350, of 50K multilingual and parallel children?s stories in 350+ languages. MCS-350 can serve as a benchmark for language identification of short texts and for 1400+ new translation directions in low-resource Indian and African languages.
1007, FreeAL: Towards Human-Free Active Learning in The Era of Large Language Models
Ruixuan Xiao; Yiwen Dong; Junbo Zhao; Runze Wu; Minmin Lin; Gang Chen; Haobo Wang;
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Highlight: It is still underexplored how to reduce the annotation cost in the LLMs era. To bridge this, we revolutionize traditional active learning and propose an innovative collaborative learning framework FreeAL to interactively distill and filter the task-specific knowledge from LLMs.
1008, Outlier Dimensions Encode Task Specific Knowledge
William Rudman; Catherine Chen; Carsten Eickhoff;
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Highlight: In this study, we investigate how fine-tuning impacts outlier dimensions and show that 1) outlier dimensions that occur in pre-training persist in fine-tuned models and 2) a single outlier dimension can complete downstream tasks with a minimal error rate.
1009, GNAT: A General Narrative Alignment Tool
Tanzir Pial; Steven Skiena;
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Highlight: We develop a general approach to narrative alignment coupling the Smith-Waterman algorithm from bioinformatics with modern text similarity metrics.
1010, Self-Ensemble of N-best Generation Hypotheses By Lexically Constrained Decoding
Ryota Miyano; Tomoyuki Kajiwara; Yuki Arase;
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Highlight: We propose a method that ensembles N-best hypotheses to improve natural language generation.
1011, R2H: Building Multimodal Navigation Helpers That Respond to Help Requests
Yue Fan; Jing Gu; Kaizhi Zheng; Xin Wang;
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Highlight: In this work, we first introduce a novel benchmark, Respond to Help Requests (R2H), to promote the development of multi-modal navigation helpers capable of responding to requests for help, utilizing existing dialog-based embodied datasets.
1012, Generative Table Pre-training Empowers Models for Tabular Prediction
Tianping Zhang; Shaowen Wang; Shuicheng Yan; Li Jian; Qian Liu;
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Highlight: In this paper, we propose TapTap, the first attempt that leverages table pre-training to empower models for tabular prediction.
1013, Unveiling The Essence of Poetry: Introducing A Comprehensive Dataset and Benchmark for Poem Summarization
Ridwan Mahbub; Ifrad Khan; Samiha Anuva; Md Shahriar; Md Tahmid Rahman Laskar; Sabbir Ahmed;
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Highlight: That being said, we propose a new task in the field of natural language understanding called ?Poem Summarization?.
1014, EDeR: Towards Understanding Dependency Relations Between Events
Ruiqi Li; Patrik Haslum; Leyang Cui;
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Highlight: Such relationships consider two events to be independent in terms of syntax and semantics, but they fail to recognize the interdependence between events. To bridge this gap, we introduce a human-annotated Event Dependency Relation dataset (EDeR).
1015, It Ain?t Over: A Multi-aspect Diverse Math Word Problem Dataset
Jiwoo Kim; Youngbin Kim; Ilwoong Baek; JinYeong Bak; Jongwuk Lee;
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Highlight: Previous studies have provided various MWP datasets but lack diversity in problem types, lexical usage patterns, languages, and annotations for intermediate solutions. To address these limitations, we introduce a new MWP dataset, named DMath (Diverse Math Word Problems), offering a wide range of diversity in problem types, lexical usage patterns, languages, and intermediate solutions.
1016, A Linear Time Approximation of Wasserstein Distance with Word Embedding Selection
Sho Otao; Makoto Yamada;
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Highlight: In this study, we propose a method to combine feature selection and tree approximation of Wasserstein distance to handle high-dimensional problems.
1017, Conversation Understanding Using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction
Cam Van Thi Nguyen; Tuan Mai; Son The; Dang Kieu; Duc-Trong Le;
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Highlight: Additionally, most existing approaches take fused features of multiple modalities in an unified input without leveraging modality-specific representations. Motivating from these problems, we propose the Relational Temporal Graph Neural Network with Auxiliary Cross-Modality Interaction (CORECT), an novel neural network framework that effectively captures conversation-level cross-modality interactions and utterance-level temporal dependencies with the modality-specific manner for conversation understanding.
1018, Connecting Degree and Polarity: An Artificial Language Learning Study
Lisa Bylinina; Alexey Tikhonov; Ekaterina Garmash;
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Highlight: We investigate a new linguistic generalisation in pre-trained language models (taking BERT Devlin et al. 2019 as a case study).
1019, A State-Vector Framework for Dataset Effects
Esmat Sahak; Zining Zhu; Frank Rudzicz;
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Highlight: However, the effects of the datasets, especially how they interact with each other, remain underexplored. We propose a state-vector framework to enable rigorous studies in this direction.
1020, Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyond
Siyang Liu; Naihao Deng; Sahand Sabour; Yilin Jia; Minlie Huang; Rada Mihalcea;
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Highlight: We propose task-adaptive tokenization as a way to adapt the generation pipeline to the specifics of a downstream task and enhance long-form generation in mental health.
1021, Making Large Language Models Better Data Creators
Dong-Ho Lee; Jay Pujara; Mohit Sewak; Ryen White; Sujay Jauhar;
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Highlight: In this paper, we propose a unified data creation pipeline that requires only a single formatting example, and which is applicable to a broad range of tasks, including traditionally problematic ones with semantically devoid label spaces.
1022, Open Information Extraction Via Chunks
Kuicai Dong; Aixin Sun; Jung-jae Kim; Xiaoli Li;
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Highlight: We instead propose Sentence as Chunk sequence (SaC) and recognize chunk spans as tuple relations and arguments.
1023, Rethinking Word-Level Auto-Completion in Computer-Aided Translation
Xingyu Chen; Lemao Liu; Guoping Huang; Zhirui Zhang; Mingming Yang; Shuming Shi; Rui Wang;
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Highlight: We introduce a measurable criterion to address this question and discover that existing WLAC models often fail to meet this criterion. Building upon this observation, we propose an effective approach to enhance WLAC performance by promoting adherence to the criterion.
1024, Automatic Transcription of Handwritten Old Occitan Language
Esteban Arias; Vallari Pai; Matthias Sch?ffel; Christian Heumann; Matthias Aenmacher;
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Highlight: In this paper, we propose an innovative HTR approach that leverages the Transformer architecture for recognizing handwritten Old Occitan language.
1025, Anaphor Assisted Document-Level Relation Extraction
Chonggang Lu; Richong Zhang; Kai Sun; Jaein Kim; Cunwang Zhang; Yongyi Mao;
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Highlight: Such an approach has difficulties in learning fine-grained interactions between entities across different sentences, resulting in sub-optimal performance. To address these issues, we propose an Anaphor-Assisted (AA) framework for DocRE tasks.
1026, FinEntity: Entity-level Sentiment Classification for Financial Texts
Yixuan Tang; Yi Yang; Allen Huang; Andy Tam; Justin Tang;
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Highlight: In this work, we introduce an entity-level sentiment classification dataset, called FinEntity, that annotates financial entity spans and their sentiment (positive, neutral, and negative) in financial news.
1027, All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison
Yujian Liu; Xinliang Zhang; Kaijian Zou; Ruihong Huang; Nicholas Beauchamp; Lu Wang;
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Highlight: We develop a latent variable-based framework to predict the ideology of news articles by comparing multiple articles on the same story and identifying partisan events whose inclusion or omission reveals ideology.
1028, ScanDL: A Diffusion Model for Generating Synthetic Scanpaths on Texts
Lena Bolliger; David Reich; Patrick Haller; Deborah Jakobi; Paul Prasse; Lena J?ger;
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Highlight: Following recent advances in adapting diffusion processes to discrete data, we propose ScanDL, a novel discrete sequence-to-sequence diffusion model that generates synthetic scanpaths on texts.
1029, From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models
Dongjun Kang; Joonsuk Park; Yohan Jo; JinYeong Bak;
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Highlight: To this end, we present Value Injection Method (VIM), a collection of two methods?argument generation and question answering?designed to inject targeted value distributions into LLMs via fine-tuning.
1030, Analyzing Film Adaptation Through Narrative Alignment
Tanzir Pial; Shahreen Aunti; Charuta Pethe; Allen Kim; Steven Skiena;
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Highlight: Novels are often adapted into feature films, but the differences between the two media usually require dropping sections of the source text from the movie script. Here we study this screen adaptation process by constructing narrative alignments using the Smith-Waterman local alignment algorithm coupled with SBERT embedding distance to quantify text similarity between scenes and book units.
1031, Inverse Scaling Can Become U-Shaped
Jason Wei; Najoung Kim; Yi Tay; Quoc Le;
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Highlight: In this paper, we evaluate models of up to 540B parameters, trained on five times more compute than those evaluated in the Inverse Scaling Prize.
1032, Nearest Neighbor Machine Translation Is Meta-Optimizer on Output Projection Layer
Ruize Gao; Zhirui Zhang; Yichao Du; Lemao Liu; Rui Wang;
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Highlight: In this paper, we comprehensively analyze kNN-MT through theoretical and empirical studies.
1033, Variance Matters: Detecting Semantic Differences Without Corpus/Word Alignment
Ryo Nagata; Hiroya Takamura; Naoki Otani; Yoshifumi Kawasaki;
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Highlight: In this paper, we propose methods for discovering semantic differences in words appearing in two corpora.
1034, Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding
Taolin Zhang; Ruyao Xu; Chengyu Wang; Zhongjie Duan; Cen Chen; Minghui Qiu; Dawei Cheng; Xiaofeng He; Weining Qian;
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Highlight: In this paper, we propose a Knowledge-enhanced language representation learning framework for various closed domains (KANGAROO) via capturing the implicit graph structure among the entities.
1035, ScdNER: Span-Based Consistency-Aware Document-Level Named Entity Recognition
Ying Wei; Qi Li;
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Highlight: This work proposes a two-stage document-level NER model, ScdNER, for more accurate and consistent predictions via adaptive span-level global feature fusion.
1036, NormDial: A Comparable Bilingual Synthetic Dialog Dataset for Modeling Social Norm Adherence and Violation
Oliver Li; Mallika Subramanian; Arkadiy Saakyan; Sky CH-Wang; Smaranda Muresan;
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Highlight: We present NormDial, a high-quality dyadic dialogue dataset with turn-by-turn annotations of social norm adherences and violations for Chinese and American cultures.
1037, Leap-of-Thought: Accelerating Transformers Via Dynamic Token Routing
Yeachan Kim; Junho Kim; Jun-Hyung Park; Mingyu Lee; SangKeun Lee;
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Highlight: In this paper, we introduce Leap-of-Thought (LoT), a novel token reduction approach that dynamically routes tokens within layers.
1038, Reinforcement Replaces Supervision: Query Focused Summarization Using Deep Reinforcement Learning
Swaroop Nath; Pushpak Bhattacharyya; Harshad Khadilkar;
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Highlight: To aid the RL training, we propose a better semantic similarity reward, enabled by a novel Passage Embedding scheme developed using Cluster Hypothesis.
1039, Fair Text Classification with Wasserstein Independence
Thibaud Leteno; Antoine Gourru; Charlotte Laclau; R?mi Emonet; Christophe Gravier;
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Highlight: This paper presents a novel method for mitigating biases in neural text classification, agnostic to the model architecture.
1040, An Attribution Method for Siamese Encoders
Lucas Moeller; Dmitry Nikolaev; Sebastian Pad?;
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Highlight: This paper derives a local attribution method for Siamese encoders by generalizing the principle of integrated gradients to models with multiple inputs.
1041, Global Voices, Local Biases: Socio-Cultural Prejudices Across Languages
Anjishnu Mukherjee; Chahat Raj; Ziwei Zhu; Antonios Anastasopoulos;
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Highlight: In this work, we scale the Word Embedding Association Test (WEAT) to 24 languages, enabling broader studies and yielding interesting findings about LM bias.
1042, Graph Vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue
Yizhe Yang; Heyan Huang; Yuhang Liu; Yang Gao;
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Highlight: Supported by statistical shreds of evidence, we offer conclusive solutions and sensible suggestions for directions and standards of future research.
1043, Are Compressed Language Models Less Subgroup Robust?
Leonidas Gee; Andrea Zugarini; Novi Quadrianto;
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Highlight: In this paper, we investigate the effects of 18 different compression methods and settings on the subgroup robustness of BERT language models.
1044, Predict and Use: Harnessing Predicted Gaze to Improve Multimodal Sarcasm Detection
Divyank Tiwari; Diptesh Kanojia; Anupama Ray; Apoorva Nunna; Pushpak Bhattacharyya;
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Highlight: In this paper, we propose the utilization of synthetic gaze data to improve the task performance for multimodal sarcasm detection in a conversational setting.
1045, Fine-grained Medical Vision-Language Representation Learning for Radiology Report Generation
Siyuan Wang; Bo Peng; Yichao Liu; Qi Peng;
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Highlight: In this study, we propose a phenotype-driven medical vision-language representation learning framework to efficiently bridge the gap between visual and textual modalities for improved text-oriented generation.
1046, Do Differences in Values Influence Disagreements in Online Discussions?
Michiel van der Meer; Piek Vossen; Catholijn Jonker; Pradeep Murukannaiah;
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Highlight: We investigate a hypothesis that differences in personal values are indicative of disagreement in online discussions.
1047, Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimoda Emotion Recognition
Dongyuan Li; Yusong Wang; Kotaro Funakoshi; Manabu Okumura;
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Highlight: In this paper, we propose a method for joint modality fusion and graph contrastive learning for multimodal emotion recognition (Joyful), where multimodality fusion, contrastive learning, and emotion recognition are jointly optimized.
1048, HyperRank: Hyperbolic Ranking Model for Unsupervised Keyphrase Extraction
Mingyang Song; Huafeng Liu; Liping Jing;
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Highlight: However, many recent unsupervised keyphrase extraction models overlook this aspect, resulting in incorrect keyphrase extraction. In this paper, we address this issue by proposing a new hyperbolic ranking model (HyperRank).
1049, Assessing The Influence of Attractor-verb Distance on Grammatical Agreement in Humans and Language Models
Christos Zacharopoulos; Th?o Desbordes; Mathias Sabl?-Meyer;
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Highlight: Here, we parametrically modulate the distance between the attractor and the verb while keeping the length of the sentence equal.
1050, Federated Meta-Learning for Emotion and Sentiment Aware Multi-modal Complaint Identification
Apoorva Singh; Siddarth Chandrasekar; Sriparna Saha; Tanmay Sen;
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Highlight: In this work, we created a new dataset - Multi-modal Complaint Dataset (MCD), a collection of reviews and images of the products posted on the website of the retail giant Amazon.
1051, Hop, Union, Generate: Explainable Multi-hop Reasoning Without Rationale Supervision
Wenting Zhao; Justin Chiu; Claire Cardie; Alexander Rush;
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Highlight: This work proposes a principled, probabilistic approach for training explainable multi-hop QA systems without rationale supervision.
1052, To Split or Not to Split: Composing Compounds in Contextual Vector Spaces
Christopher Jenkins; Filip Miletic; Sabine Schulte im Walde;
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Highlight: Using variants of BERT models and tokenization strategies on domain-specific restricted diachronic data, we introduce a suite of evaluations relying on the masked language modelling task and compositionality prediction.
1053, Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization
Chi Cheang; Hou Chan; Derek Wong; Xuebo Liu; Zhaocong Li; Yanming Sun; Shudong Liu; Lidia Chao;
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Highlight: In this work, we propose TempoSum, a novel benchmark that contains data samples from 2010 to 2022, to understand the temporal generalization ability of abstractive summarization models.
1054, Zero-Shot Multi-Label Topic Inference with Sentence Encoders and LLMs
Souvika Sarkar; Dongji Feng; Shubhra Kanti Karmaker Santu;
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Highlight: In this paper, we conducted a comprehensive study with the latest Sentence Encoders and Large Language Models (LLMs) on the challenging task of ?definition-wild zero-shot topic inference?, where users define or provide the topics of interest in real-time.
1055, TaskDiff: A Similarity Metric for Task-Oriented Conversations
Ankita Bhaumik; Praveen Venkateswaran; Yara Rizk; Vatche Isahagian;
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Highlight: While many similarity metrics have been proposed in the literature, they have not proven effective for task-oriented conversations as they do not take advantage of unique conversational features. To address this gap, we present TaskDiff, a novel conversational similarity metric that utilizes different dialogue components (utterances, intents, and slots) and their distributions to compute similarity.
1056, Not All Fake News Is Written: A Dataset and Analysis of Misleading Video Headlines
Yoo Sung; Jordan Boyd-Graber; Naeemul Hassan;
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Highlight: To complement existing resources, we present multimodal Video Misleading Headline (VMH), a dataset that consists of videos and whether annotators believe the headline is representative of the video?s contents.
1057, Euphemistic Abuse ? A New Dataset and Classification Experiments for Implicitly Abusive Language
Michael Wiegand; Jana Kampfmeier; Elisabeth Eder; Josef Ruppenhofer;
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Highlight: We address the task of identifying euphemistic abuse (e. g. ?You inspire me to fall asleep?) paraphrasing simple explicitly abusive utterances (e. g. ?You are boring?). For this task, we introduce a novel dataset that has been created via crowdsourcing.
1058, ATHENA: Mathematical Reasoning with Thought Expansion
Jb. Kim; Hazel Kim; Joonghyuk Hahn; Yo-Sub Han;
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Highlight: We introduce Attention-based THought Expansion Network Architecture (ATHENA) to tackle the challenges of real-world practices by mimicking human thought expansion mechanisms in the form of neural network propagation.
1059, A Benchmark for Reasoning with Spatial Prepositions
Iulia Comsa; Srini Narayanan;
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Highlight: We propose a novel benchmark focused on assessing inferential properties of statements with spatial prepositions.
1060, Mitigating Over-Generation for Unsupervised Keyphrase Extraction with Heterogeneous Centrality Detection
Mingyang Song; Pengyu Xu; Yi Feng; Huafeng Liu; Liping Jing;
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Highlight: Over-generation errors occur when a keyphrase extraction model correctly determines a candidate keyphrase as a keyphrase because it contains a word that frequently appears in the document but at the same time erroneously outputs other candidates as keyphrases because they contain the same word. To mitigate this issue, we propose a new heterogeneous centrality detection approach (CentralityRank), which extracts keyphrases by simultaneously identifying both implicit and explicit centrality within a heterogeneous graph as the importance score of each candidate.
1061, Can Language Models Learn Analogical Reasoning? Investigating Training Objectives and Comparisons to Human Performance
Molly Petersen; Lonneke van der Plas;
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Highlight: In this paper, we test several ways to learn basic analogical reasoning, specifically focusing on analogies that are more typical of what is used to evaluate analogical reasoning in humans than those in commonly used NLP benchmarks.
1062, Multilingual Previously Fact-Checked Claim Retrieval
Mat?? Pikuliak; Ivan Srba; Robert Moro; Timo Hromadka; Timotej Smolen; Martin Meli?ek; Ivan Vykopal; Jakub Simko; Juraj Podrou?ek; Maria Bielikova;
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Highlight: This paper introduces a new multilingual dataset for previously fact-checked claim retrieval.
1063, ALCAP: Alignment-Augmented Music Captioner
Zihao He; Weituo Hao; Wei-Tsung Lu; Changyou Chen; Kristina Lerman; Xuchen Song;
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Highlight: However, a comprehensive understanding of music necessitates the integration of both these elements. In this study, we delve into this overlooked realm by introducing a method to systematically learn multimodal alignment between audio and lyrics through contrastive learning.
1064, P5: Plug-and-Play Persona Prompting for Personalized Response Selection
Joosung Lee; Minsik Oh; Donghun Lee;
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Highlight: The use of persona-grounded retrieval-based chatbots is crucial for personalized conversations, but there are several challenges that need to be addressed. 1) In general, collecting persona-grounded corpus is very expensive. 2) The chatbot system does not always respond in consideration of persona at real applications. To address these challenges, we propose a plug-and-play persona prompting method. Our system can function as a standard open-domain chatbot if persona information is not available.
1065, Reader: Model-based Language-instructed Reinforcement Learning
Nicola Dainese; Pekka Marttinen; Alexander Ilin;
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Highlight: We explore how we can build accurate world models, which are partially specified by language, and how we can plan with them in the face of novelty and uncertainty.
1066, Adapting Offline Speech Translation Models for Streaming with Future-Aware Distillation and Inference
Biao Fu; Minpeng Liao; Kai Fan; Zhongqiang Huang; Boxing Chen; Yidong Chen; Xiaodong Shi;
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Highlight: We demonstrate that speech representations extracted at the end of a streaming input are significantly different from those extracted from a complete utterance. To address this issue, we propose a new approach called Future-Aware Streaming Translation (FAST) that adapts an offline ST model for streaming input.
1067, GenEx: A Commonsense-aware Unified Generative Framework for Explainable Cyberbullying Detection
Krishanu Maity; Raghav Jain; Prince Jha; Sriparna Saha; Pushpak Bhattacharyya;
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Highlight: While extensive research is being conducted to develop more effective models for detecting cyberbullying in monolingual languages, a significant gap exists in understanding code-mixed languages and the need for explainability in this context. To address this gap, we have introduced a novel benchmark dataset named BullyExplain for explainable cyberbullying detection in code-mixed language.
1068, Addressing Linguistic Bias Through A Contrastive Analysis of Academic Writing in The NLP Domain
Robert Ridley; Zhen Wu; Jianbing Zhang; Shujian Huang; Xinyu Dai;
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Highlight: Through our analysis, we identify that there are a number of characteristics that are highly variable across the different corpora examined in this paper.
1069, Speech Recognition and Meaning Interpretation: Towards Disambiguation of Structurally Ambiguous Spoken Utterances in Indonesian
Ruhiyah Widiaputri; Ayu Purwarianti; Dessi Lestari; Kurniawati Azizah; Dipta Tanaya; Sakriani Sakti;
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Highlight: In this study, we attempt to resolve structurally ambiguous utterances into unambiguous texts in Indonesian using prosodic information.
1070, Target-Agnostic Gender-Aware Contrastive Learning for Mitigating Bias in Multilingual Machine Translation
Minwoo Lee; Hyukhun Koh; Kang-il Lee; Dongdong Zhang; Minsung Kim; Kyomin Jung;
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Highlight: In this paper, we specifically target the gender bias issue of multilingual machine translation models for unambiguous cases where there is a single correct translation, and propose a bias mitigation method based on a novel approach.
1071, Code-Switching Metrics Using Intonation Units
Rebecca Pattichis; Dora LaCasse; Sonya Trawick; Rena Cacoullos;
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Highlight: Crucially, CS is not equally likely between any two words, but follows syntactic and prosodic rules. We adapt two metrics, multilinguality and CS probability, and apply them to transcribed bilingual speech, for the first time putting forward Intonation Units (IUs) ? prosodic speech segments ? as basic tokens for NLP tasks.
1072, BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis
Tingfeng Cao; Chengyu Wang; Bingyan Liu; Ziheng Wu; Jinhui Zhu; Jun Huang;
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Highlight: We propose BeautifulPrompt, a deep generative model to produce high-quality prompts from very simple raw descriptions, which enables diffusion-based models to generate more beautiful images.
1073, Enhancing Language Model with Unit Test Techniques for Efficient Regular Expression Generation
Chenhui Mao; Xiexiong Lin; Xin Jin; Xin Zhang;
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Highlight: However, these approaches have shown shortcomings in practical applications, particularly in terms of functional correctness, which refers to the ability to reproduce the intended function inputs by the user. To address this issue, we present a novel method called Unit-Test Driven Reinforcement Learning (UTD-RL).
1074, A Comparative Analysis of Task-Agnostic Distillation Methods for Compressing Transformer Language Models
Takuma Udagawa; Aashka Trivedi; Michele Merler; Bishwaranjan Bhattacharjee;
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Highlight: In this paper, we reproduce, compare and analyze several representative methods for task-agnostic (general-purpose) distillation of Transformer language models.
1075, CDD: A Large Scale Dataset for Legal Intelligence Research
Changzhen Ji; Yating Zhang; Adam Jatowt; Haipang Wu;
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Highlight: In this paper, we present a novel, large-size Court Debate Dataset (CDD), which includes 30,481 court cases, totaling 1,144,425 utterances.
1076, MUST&P-SRL: Multi-lingual and Unified Syllabification in Text and Phonetic Domains for Speech Representation Learning
No? Tits;
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Highlight: In this paper, we present a methodology for linguistic feature extraction, focusing particularly on automatically syllabifying words in multiple languages, with a design to be compatible with a forced-alignment tool, the Montreal Forced Aligner (MFA).
1077, Personalized Dense Retrieval on Global Index for Voice-enabled Conversational Systems
Masha Belyi; Charlotte Dzialo; Chaitanya Dwivedi; Prajit Muppidi; Kanna Shimizu;
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Highlight: In this work, we propose a personalized entity retrieval system that is robust to phonetic noise and ambiguity but is not limited to a personalized index.
1078, Deep Metric Learning to Hierarchically Rank - An Application in Product Retrieval
Kee Kiat Koo; Ashutosh Joshi; Nishaanth Reddy; Karim Bouyarmane; Ismail Tutar; Vaclav Petricek; Changhe Yuan;
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Highlight: In this paper, we develop a model to identify duplicate and near-duplicate products across stores.
1079, A Pretrained Language Model for Cyber Threat Intelligence
Youngja Park; Weiqiu You;
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Highlight: We present a new BERT model for the cybersecurity domain, CTI-BERT, which can improve the accuracy of cyber threat intelligence (CTI) extraction, enabling organizations to better defend against potential cyber threats.
1080, SAMP: A Model Inference Toolkit of Post-Training Quantization for Text Processing Via Self-Adaptive Mixed-Precision
Rong Tian; Zijing Zhao; Weijie Liu; Haoyan Liu; Weiquan Mao; Zhe Zhao; Kan Zhou;
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Highlight: In this paper, we develop a toolkit for users to easily quantize their models for inference, in which Self-Adaptive Mixed-Precision (SAMP) is proposed to automatically control quantization rate by a mixed-precision architecture to balance model accuracy and efficiency.
1081, KD-Boost: Boosting Real-Time Semantic Matching in E-commerce with Knowledge Distillation
Sanjay Agrawal; Vivek Sembium; Ankith M S;
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Highlight: In this paper, we propose KD-Boost, a novel knowledge distillation algorithm designed for real-time semantic matching.
1082, Does Named Entity Recognition Truly Not Scale Up to Real-world Product Attribute Extraction?
Wei-Te Chen; Keiji Shinzato; Naoki Yoshinaga; Yandi Xia;
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Highlight: In this study, we argue the scalability of the NER-based approach compared to the QA-based approach, since researchers have compared BERT-based QA-based models to only a weak BiLSTM-based NER baseline trained from scratch in terms of only accuracy on datasets designed to evaluate the QA-based approach.
1083, TMID: A Comprehensive Real-world Dataset for Trademark Infringement Detection in E-Commerce
Tongxin Hu; Zhuang Li; Xin Jin; Lizhen Qu; Xin Zhang;
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Highlight: Through this study, we aim to contribute valuable resources to advance research into legal compliance related to trademark infringement within the e-commerce sphere.
1084, Joint Dialogue Topic Segmentation and Categorization: A Case Study on Clinical Spoken Conversations
Zhengyuan Liu; Siti Umairah Md Salleh; Hong Choon Oh; Pavitra Krishnaswamy; Nancy Chen;
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Highlight: In this work, we introduce and adopt a joint model for dialogue segmentation and topic categorization, and conduct a case study on healthcare follow-up calls for diabetes management; we provide insights from both data and model perspectives toward performance and robustness.
1085, Retrieval-Enhanced Dual Encoder Training for Product Matching
Justin Chiu;
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Highlight: In this paper, we propose a two-stage training for the dual encoder model.
1086, Lattice Path Edit Distance: A Romanization-aware Edit Distance for Extracting Misspelling-Correction Pairs from Japanese Search Query Logs
Nobuhiro Kaji;
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Highlight: However, the success does not readily apply to Japanese, where misspellings are often dissimilar to correct spellings due to the romanization-based input methods. To address this problem, we introduce lattice path edit distance, which utilizes romanization lattices to efficiently consider all possible romanized forms of input strings.
1087, Unveiling Identity Biases in Toxicity Detection : A Game-Focused Dataset and Reactivity Analysis Approach
Josiane Van Dorpe; Zachary Yang; Nicolas Grenon-Godbout; Gr?goire Winterstein;
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Highlight: We propose a dataset and a method to highlight oversensitive terms using reactivity analysis and the model?s performance.
1088, ORANGE: Text-video Retrieval Via Watch-time-aware Heterogeneous Graph Contrastive Learning
Yucheng Lin; Tim Chang; Yaning Chang; Jianqiang Ma; Donghui Li; Ting Peng; Zang Li; Zhiyi Zhou; Feng Wang;
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Highlight: In order to accommodate various search requirements and enhance user satisfaction, this study introduces a novel Text-video Retrieval method via Watch-time-aware Heterogeneous Graph Contrastive Learning (termed ORANGE).
1089, Compute-Efficient Churn Reduction for Conversational Agents
Christopher Hidey; Sarthak Sarthak;
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Highlight: In this setting, compute resources are often limited due to latency requirements during serving and overall time constraints during re-training. To address this issue, we propose a compute-efficient method that mitigates churn without requiring extra resources for training or inference.
1090, Enhancing Extreme Multi-Label Text Classification: Addressing Challenges in Model, Data, and Evaluation
Dan Li; Zi Long Zhu; Janneke van de Loo; Agnes Masip Gomez; Vikrant Yadav; Georgios Tsatsaronis; Zubair Afzal;
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Highlight: Extreme multi-label text classification is a prevalent task in industry, but it frequently encounters challenges in terms of machine learning perspectives, including model limitations, data scarcity, and time-consuming evaluation. This paper aims to mitigate these issues by introducing novel approaches.
1091, Query-aware Multi-modal Based Ranking Relevance in Video Search
Chengcan Ye; Ting Peng; Tim Chang; Zhiyi Zhou; Feng Wang;
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Highlight: Recent multi-modal models have demonstrated promise in various vision-language tasks but provide limited help for downstream query-video relevance tasks due to the discrepency between relevance ranking-agnostic pre-training objectives and the real video search scenarios that demand comprehensive relevance modeling. To address these challenges, we propose a QUery-Aware pre-training model with multi-modaLITY (QUALITY) that incorporates hard-mined query information as alignment targets and utilizes video tag information for guidance.
1092, Creator Context for Tweet Recommendation
Spurthi Amba Hombaiah; Tao Chen; Mingyang Zhang; Michael Bendersky; Marc Najork; Matt Colen; Sergey Levi; Vladimir Ofitserov; Tanvir Amin;
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Highlight: In this paper, we attempt to answer the question of how creator context should be used to advance tweet understanding.
1093, AdaBERT-CTC: Leveraging BERT-CTC for Text-Only Domain Adaptation in ASR
Tyler Vuong; Karel Mundnich; Dhanush Bekal; Veera Elluru; Srikanth Ronanki; Sravan Bodapati;
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Highlight: In this paper we introduce AdaBERT-CTC, a domain adaptation technique that relies solely on textual data.
1094, Conversing with Databases: Practical Natural Language Querying
Denis Kochedykov; Fenglin Yin; Sreevidya Khatravath;
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Highlight: In this work, we designed, developed and released in production DataQue ? a hybrid NLQ (Natural Language Querying) system for conversational DB querying.
1095, Speakerly: A Voice-based Writing Assistant for Text Composition
Dhruv Kumar; Vipul Raheja; Alice Kaiser-Schatzlein; Robyn Perry; Apurva Joshi; Justin Hugues-Nuger; Samuel Lou; Navid Chowdhury;
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Highlight: We present Speakerly, a new real-time voice-based writing assistance system that helps users with text composition across various use cases such as emails, instant messages, and notes.
1096, CL-QR: Cross-Lingual Enhanced Query Reformulation for Multi-lingual Conversational AI Agents
Zhongkai Sun; Zhengyang Zhao; Sixing Lu; Chengyuan Ma; Xiaohu Liu; Xing Fan; Wei Shen; Chenlei Guo;
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Highlight: This work proposes a novel cross-lingual QR framework, CL-QR, to leverage the abundant reformulation resources in English to improve non-English QR performance.
1097, Improving Contextual Query Rewrite for Conversational AI Agents Through User-preference Feedback Learning
Zhongkai Sun; Yingxue Zhou; Jie Hao; Xing Fan; Yanbin Lu; Chengyuan Ma; Wei Shen; Chenlei Guo;
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Highlight: Inspired by recent advances in learning from human feedback (LHF), this paper proposes a novel Preference Aligned Contextual Query Rewriting (PA-CQR) framework to enhance the CQR model?s capability in generating user preference-aligned rewrites.
1098, Scaling Neural ITN for Numbers and Temporal Expressions in Tamil: Findings for An Agglutinative Low-resource Language
Bhavuk Singhal; Sindhuja Gopalan; Amrith Krishna; Malolan Chetlur;
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Highlight: Being morphologically rich, the words in Tamil show a high degree of agglutination due to inflection and clitics. The combination of such factors leads to a high degree of surface-form variations, making scalability with pure rule-based approaches difficult. Instead, we experiment with fine-tuning three pre-trained neural LMs, consisting of a seq2seq model (s2s), a non-autoregressive text editor (NAR) and a sequence tagger + rules combination (tagger).
1099, Gold Standard Bangla OCR Dataset: An In-Depth Look at Data Preprocessing and Annotation Processes
Hasmot Ali; AKM Shahariar Azad Rabby; Md Majedul Islam; A.k.m Mahamud; Nazmul Hasan; Fuad Rahman;
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Highlight: This study introduces the most extensive gold standard corpus for Bangla characters and words, comprising over 4 million human-annotated images.
1100, PILLOW: Enhancing Efficient Instruction Fine-tuning Via Prompt Matching
Zhenting Qi; Xiaoyu Tan; Shaojie Shi; Chao Qu; Yinghui Xu; Yuan Qi;
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Highlight: In this paper, we propose PILLOW, which aims to improve LoRA?s performance by leveraging LLM?s in-context learning capability through prompt matching via reinforcement learning in resource-constrained environments.
1101, Welcome to The Real World: Efficient, Incremental and Scalable Key Point Analysis
Lilach Eden; Yoav Kantor; Matan Orbach; Yoav Katz; Noam Slonim; Roy Bar-Haim;
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Highlight: This work presents a deployed KPA system, which regularly serves multiple teams in our organization.
1102, Automatic Linking of Judgements to UK Supreme Court Hearings
Hadeel Saadany; Constantin Orasan;
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Highlight: In this research, we summarise the second part of a combined research-industrial project for building an automated tool designed specifically to link segments in the text judgement to semantically relevant timespans in the videos of the hearings.
1103, Automatic Marketing Theme and Commodity Construction System for E-commerce
Zhiping Wang; Peng Lin; Hainan Zhang; Hongshen Chen; Tianhao Li; Zhuoye Ding; Sulong Xu; Jinghe Hu;
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Highlight: However, the current system invites experts to write marketing themes and select the relevant commodities, which suffer from difficulty in mass production, poor timeliness and low online indicators. Therefore, we propose a automatic marketing theme and commodity construction system, which can not only generate popular marketing themes and select the relevant commodities automatically, but also improve the theme online effectiveness in the recommendation system.
1104, Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures
Shumpei Inoue; Minh-Tien Nguyen; Hiroki Mizokuchi; Tuan-Anh Nguyen; Huu-Hiep Nguyen; Dung Le;
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Highlight: This paper introduces a new IncidentAI dataset for safety prevention.
1105, An Auxiliary Task Boosted Multi-task Learning Method for Service Account Retrieval with Limited Human Annotation
Yuanzhou Yao; Zhao Zhang; Kaijia Yang; Huasheng Liang; Qiang Yan; Yongjun Xu;
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Highlight: To this end, this paper proposes a novel approach, the Auxiliary task Boosted Multi-Task Learning method (AuxBoost-MTL).
1106, VKIE: The Application of Key Information Extraction on Video Text
Siyu An; Ye Liu; Haoyuan Peng; Di Yin;
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Highlight: In this paper, we define a significant task of extracting hierarchical key information from visual texts on videos.
1107, Investigating The Role and Impact of Disfluency on Summarization
Varun Nathan; Ayush Kumar; Jithendra Vepa;
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Highlight: To mitigate this, we examine Fused-Fine Tuning by training the model with a combination of fluent and disfluent data, resulting in improved performance on both public and real-life datasets. Our work highlights the significance of incorporating disfluency in training summarization models and its advantages in an industrial setting.
1108, InsightNet : Structured Insight Mining from Customer Feedback
Sandeep Sricharan Mukku; Manan Soni; Chetan Aggarwal; Jitenkumar Rana; Promod Yenigalla; Rashmi Patange; Shyam Mohan;
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Highlight: We propose InsightNet, a novel approach for the automated extraction of structured insights from customer reviews.
1109, E2E Spoken Entity Extraction for Virtual Agents
Karan Singla; Yeon-Jun Kim; Srinivas Bangalore;
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Highlight: In this paper, we study the impact of fine-tuning pre-trained speech encoders on extracting spoken entities in human-readable form directly from speech without the need for text transcription.
1110, Generative Models for Product Attribute Extraction
Ansel Blume; Nasser Zalmout; Heng Ji; Xian Li;
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Highlight: In this work, we explore the use of generative models for product attribute extraction.
1111, CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering
Md Rashad Al Hasan Rony; Christian Suess; Sinchana Ramakanth Bhat; Viju Sudhi; Julia Schneider; Maximilian Vogel; Roman Teucher; Ken Friedl; Soumya Sahoo;
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Highlight: In this paper, we propose CarExpert, an in-car retrieval-augmented conversational question-answering system leveraging LLMs for different tasks.
1112, Multi-word Tokenization for Sequence Compression
Leonidas Gee; Leonardo Rigutini; Marco Ernandes; Andrea Zugarini;
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Highlight: In this paper, we present MWT: a Multi-Word Tokenizer that goes beyond word boundaries by representing frequent multi-word expressions as single tokens.
1113, JarviX: A LLM No Code Platform for Tabular Data Analysis and Optimization
Shang-Ching Liu; ShengKun Wang; Tsungyao Chang; Wenqi Lin; Chung-Wei Hsiung; Yi-Chen Hsieh; Yu-Ping Cheng; Sian-Hong Luo; Jianwei Zhang;
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Highlight: In this study, we introduce JarviX, a sophisticated data analytics framework.
1114, Retrieve and Copy: Scaling ASR Personalization to Large Catalogs
Sai Muralidhar Jayanthi; Devang Kulshreshtha; Saket Dingliwal; Srikanth Ronanki; Sravan Bodapati;
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Highlight: However, due to performance constraints, the biasing is often limited to a few thousand entities, restricting real-world usability. To address this, we first propose a ?Retrieve and Copy? mechanism to improve latency while retaining the accuracy even when scaled to a large catalog.
1115, STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants
Leon Zhang; Jiarui Lu; Joel Ruben Antony Moniz; Aditya Kulkarni; Dhivya Piraviperumal; Tien Dung Tran; Nick Tzou; Hong Yu;
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Highlight: We propose STEER, a steering detection model that predicts whether a follow-up turn is a user?s attempt to steer the previous command.
1116, Self-Criticism: Aligning Large Language Models with Their Understanding of Helpfulness, Honesty, and Harmlessness
Xiaoyu Tan; Shaojie Shi; Xihe Qiu; Chao Qu; Zhenting Qi; Yinghui Xu; Yuan Qi;
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Highlight: Therefore, we explore the possibility of aligning LLMs with their own understanding of HHH through IF and in-context learning (ICL). In this study, we propose a novel framework called Self-Criticism, which allows LLMs to align themselves with HHH based on the definition they learned from a large-scale text corpus.
1117, InstructPTS: Instruction-Tuning LLMs for Product Title Summarization
Besnik Fetahu; Zhiyu Chen; Oleg Rokhlenko; Shervin Malmasi;
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Highlight: Inspired by recent work on instruction-tuned LLMs, we present InstructPTS, a controllable approach for the task of Product Title Summarization (PTS).
1118, DUBLIN: Visual Document Understanding By Language-Image Network
Kriti Aggarwal; Aditi Khandelwal; Kumar Tanmay; Owais Khan Mohammed; Qiang Liu; Monojit Choudhury; Hardik Chauhan; Subhojit Som; Vishrav Chaudhary; Saurabh Tiwary;
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Highlight: In this paper, we present DUBLIN, a pixel-based model for visual document understanding that does not rely on OCR.
1119, Relevance-assisted Generation for Robust Zero-shot Retrieval
Jihyuk Kim; Minsoo Kim; Joonsuk Park; Seung-won Hwang;
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Highlight: Our contribution is showing that key biases can cause sampled PQ to be irrelevant, negatively contributing to generalization.
1120, Too Much of Product Information : Don?t Worry, Let?s Look for Evidence!
Aryan Jain; Jitenkumar Rana; Chetan Aggarwal;
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Highlight: In this paper, we propose a distantly supervised solution to answer customer questions by using product information.
1121, Adaptive Hyper-parameter Learning for Deep Semantic Retrieval
Mingming Li; Chunyuan Yuan; Huimu Wang; Peng Wang; Jingwei Zhuo; Binbin Wang; Lin Liu; Sulong Xu;
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Highlight: We argue that those are not suitable for retrieval scenarios, due to the agnosticism and diversity of the queries. To fully overcome this limitation, we propose a novel adaptive metric learning method that designs a simple and universal hyper-parameter-free learning method to improve the performance of retrieval.
1122, On Sample-Efficient Code Generation
Hojae Han; Yu Jin Kim; Byoungjip Kim; Youngwon Lee; Kyungjae Lee; Kyungmin Lee; Moontae Lee; Kyunghoon Bae; Seung-won Hwang;
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Highlight: We introduce EFFICODE, a novel framework that prioritizes sampling on test problems that models can solve.
1123, DELPHI: Data for Evaluating LLMs? Performance in Handling Controversial Issues
David Sun; Artem Abzaliev; Hadas Kotek; Christopher Klein; Zidi Xiu; Jason Williams;
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Highlight: However, few such datasets exist in providing human-annotated labels reflecting the contemporary discussions. To foster research in this area, we propose a novel construction of a controversial questions dataset, expanding upon the publicly released Quora Question Pairs Dataset.
1124, Angel: Enterprise Search System for The Non-Profit Industry
Saiful Haq; Ashutosh Sharma; Pushpak Bhattacharyya;
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Highlight: In this paper, we create an enterprise search system ?ANGEL? for the non-profit industry that takes a fund-giver?s mission description as input and returns a ranked list of fund-seekers as output, and vice-versa.