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检索条件"任意字段=Conference on empirical methods in natural language processing"

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Ecologically Valid Explanations for Label Variation in NLI

Ecologically Valid Explanations for Label Variation in NLI

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Jiang, Nan-Jiang Tan, Chenhao De Marneffe, Marie-Catherine Ohio State Univ Dept Linguist Columbus OH 43210 USA Univ Chicago Dept Comp Sci Chicago IL 60637 USA UCLouvain FNRS Louvain Belgium

ISBN: (纸本)9798891760615

Human label variation exists in many natural language processing (NLP) tasks, including natural language inference (NLI). To gain direct evidence of how NLI label variation arises, we build LIVENLI, an English dataset of 1,415 ecologically valid explanations (annotators explain the NLI labels they chose) for 122 MNLI (Williams et al., 2018) items (at least 10 explanations per item). The LIVENLI explanations confirm that people can systematically vary on their interpretation and highlight within-label variation: annotators sometimes choose the same label for different reasons. This suggests that explanations are crucial for navigating label interpretations in general. We few-shot prompt language models (LMs) to generate explanations but the results are inconsistent: the models sometimes produce valid and informative explanations, but they also generate implausible ones that do not support the label, highlighting directions for improvement.

关键词： natural language processing systems

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Are Large Vision language Models up to the Challenge of Chart Comprehension and Reasoning?

Are Large Vision Language Models up to the Challenge of Char...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Islam, Mohammed Saidul Rahman, Raian Masry, Ahmed Laskar, Md Tahmid Rahman Nayeem, Mir Tafseer Hoque, Enamul York University Canada Islamic University of Technology Bangladesh University of Alberta Canada Dialpad Inc. Canada

ISBN: (纸本)9798891761681

natural language is a powerful complementary modality of communication for data visualizations, such as bar and line charts. To facilitate chart-based reasoning using natural language, various downstream tasks have been introduced recently such as chart question answering, chart summarization, and fact-checking with charts. These tasks pose a unique challenge, demanding both vision-language reasoning and a nuanced understanding of chart data tables, visual encodings, and natural language instructions. Despite the recent success of Large language Models (LLMs) across diverse NLP tasks, their abilities and limitations in the realm of data visualization remain under-explored, possibly due to their lack of multi-modal capabilities. To bridge the gap, this paper presents one of the first comprehensive evaluations of the recently developed large vision language models (LVLMs) for chart understanding and reasoning tasks. Our evaluation includes a comprehensive assessment of both closed and open-sourced LVLMs across five major chart reasoning tasks. Furthermore, we perform a qualitative evaluation of LVLMs' performance on a diverse range of charts, aiming to provide a thorough analysis. Our findings reveal that while LVLMs demonstrate impressive abilities in generating fluent texts covering high-level data insights, they also encounter common problems like hallucinations, factual errors, and data bias. We highlight the key strengths and limitations of LVLMs in chart comprehension tasks, offering insights for future research. © 2024 Association for Computational Linguistics.

关键词： Visual languages

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Adaptive Query Rewriting: Aligning Rewriters through Marginal Probability of Conversational Answers

Adaptive Query Rewriting: Aligning Rewriters through Margina...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Zhang, Tianhua Li, Kun Luo, Hongyin Wu, Xixin Glass, James Meng, Helen The Chinese University of Hong Kong Hong Kong Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9798891761643

Query rewriting is a crucial technique for passage retrieval in open-domain conversational question answering (CQA). It decontexualizes conversational queries into self-contained questions suitable for off-the-shelf retrievers. Existing methods attempt to incorporate retriever's preference during the training of rewriting models. However, these approaches typically rely on extensive annotations such as in-domain rewrites and/or relevant passage labels, limiting the models' generalization and adaptation capabilities. In this paper, we introduce AdaQR (Adaptive Query Rewriting), a framework for training query rewriting models with limited rewrite annotations from seed datasets and completely no passage label. Our approach begins by fine-tuning compact large language models using only ~10% of rewrite annotations from the seed dataset training split. The models are then utilized to self-sample rewrite candidates for each query instance, further eliminating the expense for human labeling or larger language model prompting often adopted in curating preference data. A novel approach is then proposed to assess retriever's preference for these candidates with the probability of answers conditioned on the conversational query by marginalizing the Top-K passages. This serves as the reward for optimizing the rewriter further using Direct Preference Optimization (DPO), a process free of rewrite and retrieval annotations. Experimental results on four open-domain CQA datasets demonstrate that AdaQR not only enhances the in-domain capabilities of the rewriter with limited annotation requirement, but also adapts effectively to out-of-domain datasets. © 2024 Association for Computational Linguistics.

关键词： Structured Query language

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Cost-Performance Optimization for processing Low-Resource language Tasks Using Commercial LLMs

Cost-Performance Optimization for Processing Low-Resource La...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Nag, Arijit Mukherjee, Animesh Ganguly, Niloy Chakrabarti, Soumen IIT Kharagpur India IIT Bombay India

ISBN: (纸本)9798891761681

Large language Models (LLMs) exhibit impressive zero/few-shot inference and generation quality for high-resource languages (HRLs). A few of them have been trained on low-resource languages (LRLs) and give decent performance. Owing to the prohibitive costs of training LLMs, they are usually used as a network service, with the client charged by the count of input and output tokens. The number of tokens strongly depends on the script and language, as well as the LLM's subword vocabulary. We show that LRLs are at a pricing disadvantage, because the well-known LLMs produce more tokens for LRLs than HRLs. This is because most currently popular LLMs are optimized for HRL vocabularies. Our objective is to level the playing field: reduce the cost of processing LRLs in contemporary LLMs while ensuring that predictive and generative qualities are not compromised. As means to reduce the number of tokens processed by the LLM, we consider code-mixing, translation, and transliteration of LRLs to HRLs. We perform an extensive study using the IndicXTREME classification and six generative tasks dataset, covering 15 Indic and 3 other languages, while using GPT-4 (one of the costliest LLM services released so far1) as a commercial LLM. We observe and analyze interesting patterns involving token count, cost, and quality across a multitude of languages and tasks. We show that choosing the best policy to interact with the LLM can reduce cost by ∼90% while giving better or comparable performance, compared to communicating with the LLM in the original LRL. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Encouraging Divergent Thinking in Large language Models through Multi-Agent Debate

Encouraging Divergent Thinking in Large Language Models thro...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Liang, Tian He, Zhiwei Jiao, Wenxiang Wang, Xing Wang, Yan Wang, Rui Yang, Yujiu Shi, Shuming Tu, Zhaopeng Tsinghua University China Shanghai Jiao Tong University China Tencent AI Lab China

ISBN: (纸本)9798891761643

Modern large language models (LLMs) like ChatGPT have shown remarkable performance on general language tasks but still struggle on complex reasoning tasks, which drives the research on cognitive behaviors of LLMs to explore human-like problem-solving strategies. Along this direction, one representative strategy is self-reflection, which asks an LLM to refine the solution with the feedback generated by itself iteratively. However, our study shows that such reflection-style methods suffer from the Degeneration-of-Thought (DoT) problem: once the LLM has established confidence in its solutions, it is unable to generate novel thoughts later through reflection even if its initial stance is incorrect. To address the DoT problem, we propose a Multi-Agent Debate (MAD) framework, in which multiple agents express their arguments in the state of "tit for tat" and a judge manages the debate process to obtain a final solution. Clearly, our MAD framework encourages divergent thinking in LLMs which would be helpful for tasks that require deep levels of contemplation. Experiment results on two challenging datasets, commonsense machine translation and counter-intuitive arithmetic reasoning, demonstrate the effectiveness of our MAD framework. Extensive analyses suggest that the adaptive break of debate and the modest level of "tit for tat" state are required for MAD to obtain good performance. Moreover, we find that LLMs might not be a fair judge if different LLMs are used for agents. Code is available at https://***/Skytliang/Multi-Agents-Debate. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Research on Digital Economy Data processing System Based on Artificial Intelligence Algorithm 24

Research on Digital Economy Data Processing System Based on ...

引用

International conference on Modeling, natural language processing and Machine Learning (CMNM)

作者： Hao Wenfei Shandong Univ Technol Law Sch Zibo Shandong Provin Peoples R China

ISBN: (纸本)9798400709760

In contemporary society, the advent of the digital economy is swiftly emerging as a novel catalyst propelling global economic advancement. Against this backdrop, the adept management of vast economic datasets has become a focal point of inquiry. Leveraging its robust data processing and learning capabilities, artificial intelligence algorithms offer fresh perspectives on the analysis and application of digital economic data. This discourse introduces sophisticated algorithms such as kernel principal component analysis, time series analysis, and support vector machines, culminating in the conception and realization of a data processing framework. empirical analysis of real-world data validates the efficacy of the system, juxtaposed with comparative analyses to delve into the merits and limitations of various algorithms in digital economic data management. Ultimately, this research endeavors to furnish the domain of digital economics with scientifically grounded data processing methodologies and pragmatic strategic solutions.

关键词： Artificial intelligence algorithms digital economy data processing systems

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Who Wrote it and Why? Prompting Large-language Models for Authorship Verification

Who Wrote it and Why? Prompting Large-Language Models for Au...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Hung, Chia-Yu Hu, Zhiqiang Hu, Yujia Lee, Roy Ka-Wei Singapore Univ Technol & Design Singapore Singapore

ISBN: (纸本)9798891760615

Authorship verification (AV) is a fundamental task in natural language processing (NLP) and computational linguistics, with applications in forensic analysis, plagiarism detection, and identification of deceptive content. Existing AV techniques, including traditional stylometric and deep learning approaches, face limitations in terms of data requirements and lack of explainability. To address these limitations, this paper proposes PromptAV, a novel technique that leverages Large-language Models (LLMs) for AV by providing step-by-step stylometric explanation prompts. PromptAV outperforms state-of-the-art baselines, operates effectively with limited training data, and enhances interpretability through intuitive explanations, showcasing its potential as an effective and interpretable solution for the AV task.

关键词： Computational linguistics

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An empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal Models

An Empirical Analysis on Spatial Reasoning Capabilities of L...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Shiri, Fatemeh Guo, Xiao-Yu Far, Mona Golestan Yu, Xin Haffari, Gholamreza Li, Yuan-Fang Department of Data Science & AI Monash University Australia Australian Institute for Machine Learning University of Adelaide Australia School of Electrical Engineering and Computer Science University of Queensland Australia

ISBN: (纸本)9798891761643

Large Multimodal Models (LMMs) have achieved strong performance across a range of vision and language tasks. However, their spatial reasoning capabilities are under-investigated. In this paper, we construct a novel VQA dataset, Spatial-MM, to comprehensively study LMMs' spatial understanding and reasoning capabilities. Our analyses on object-relationship and multi-hop reasoning reveal several important findings. Firstly, bounding boxes and scene graphs, even synthetic ones, can significantly enhance LMMs' spatial reasoning. Secondly, LMMs struggle more with questions posed from the human perspective than the camera perspective about the image. Thirdly, chain of thought (CoT) prompting does not improve model performance on complex multi-hop questions involving spatial relations. Lastly, our perturbation analysis on GQA-spatial reveals that LMMs are much stronger at basic object detection than complex spatial reasoning. We believe our new benchmark dataset and in-depth analyses can spark further research on LMMs spatial reasoning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Learning to Refine with Fine-Grained natural language Feedback

Learning to Refine with Fine-Grained Natural Language Feedba...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Wadhwa, Manya Zhao, Xinyu Li, Junyi Jessy Durrett, Greg The University of Texas Austin United States

ISBN: (纸本)9798891761681

Recent work has explored the capability of large language models (LLMs) to identify and correct errors in LLM-generated responses. These refinement approaches frequently evaluate what sizes of models are able to do refinement for what problems, but less attention is paid to what effective feedback for refinement looks like. In this work, we propose looking at refinement with feedback as a composition of three distinct LLM competencies: (1) detection of bad generations;(2) fine-grained natural language critique generation;(3) refining with fine-grained feedback. The first step can be implemented with a high-performing discriminative model and steps 2 and 3 can be implemented either via prompted or fine-tuned LLMs. A key property of the proposed DETECT, CRITIQUE, REFINE ("DCR") method is that the step 2 critique model can give fine-grained feedback about errors, made possible by offloading the discrimination to a separate model in step 1. We show that models of different capabilities benefit from refining with DCR on the task of improving factual consistency of document grounded summaries. Overall, DCR consistently outperforms existing end-to-end refinement approaches and current trained models not fine-tuned for factuality critiquing. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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PDF-to-Tree: Parsing PDF Text Blocks into a Tree

PDF-to-Tree: Parsing PDF Text Blocks into a Tree

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Zhang, Yue Zhang, Zhihao Lai, Wenbin Zhang, Chong Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China Institute of Modern Languages and Linguistics Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (纸本)9798891761681

In many PDF documents, the reading order of text blocks is missing, which can hinder machine understanding of the document's content. Existing works try to extract one universal reading order for a PDF file. However, applications, like Retrieval Augmented Generation (RAG), require breaking long articles into sections, subsections and table cells for better indexing. For this reason, this paper introduces a new task and dataset, PDF-to-Tree, which organizes the text blocks of a PDF into a tree structure. Since a PDF may contain thousands of text blocks, this paper proposes a transition-based parser that uses a greedy strategy to build the tree structure. Compared to the parser for plain text, we also use multi-modal features to encode the parser state. Experiments show that our approach achieves an accuracy of 93.93%, surpassing the performance of baseline methods by an improvement of 6.72%. The dataset is public available at https://***/yuezh000/PDFto-Tree. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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