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

共 15258 条记录，以下是611-620 订阅

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VarBench: Robust language Model Benchmarking Through Dynamic Variable Perturbation

VarBench: Robust Language Model Benchmarking Through Dynamic...

引用

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

作者： Qian, Kun Wan, Shunji Tang, Claudia Wang, Youzhi Zhang, Xuanming Chen, Maximillian Yu, Zhou Columbia University United States

ISBN: (纸本)9798891761681

As large language models achieve impressive scores on traditional benchmarks, an increasing number of researchers are becoming concerned about benchmark data leakage during pre-training, commonly known as the data contamination problem. To ensure fair evaluation, recent benchmarks release only the training and validation sets, keeping the test set labels closed-source. They require anyone wishing to evaluate his language model to submit the model's predictions for centralized processing and then publish the model's result on their leaderboard. However, this submission process is inefficient and prevents effective error analysis. To address this issue, we propose to variabilize benchmarks and evaluate language models dynamically1. Specifically, we extract variables from each test case and define a value range for each variable. For each evaluation, we sample new values from these value ranges to create unique test cases, thus ensuring a fresh evaluation each time. We applied this variable perturbation method to four datasets: GSM8K, ARC, CommonsenseQA, and TruthfulQA, which cover mathematical generation and multiple-choice tasks. Our experimental results demonstrate that this approach provides a more accurate assessment of the true capabilities of language models, effectively mitigating the contamination problem. © 2024 Association for Computational Linguistics.

关键词： Information leakage

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Assistive Large language Model Agents for Socially-Aware Negotiation Dialogues

Assistive Large Language Model Agents for Socially-Aware Neg...

引用

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

作者： Hua, Yuncheng Qu, Lizhen Haffari, Gholamreza Department of Data Science & AI Monash University Australia

ISBN: (纸本)9798891761681

We develop assistive agents based on Large language Models (LLMs) that aid interlocutors in business ***, we simulate business negotiations by letting two LLM-based agents engage in role play.A third LLM acts as a remediator agent to rewrite utterances violating norms for improving negotiation *** introduce a simple tuning-free and label-free In-Context Learning (ICL) method to identify high-quality ICL exemplars for the remediator, where we propose a novel select criteria, called value impact, to measure the quality of the negotiation *** provide rich empirical evidence to demonstrate its effectiveness in negotiations across three different negotiation *** have released our source code and the generated dataset at: https://***/tk1363704/SADAS. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Multi-Task Corpus for Assessing Discourse Coherence in Chinese Essays: Structure, Theme, and Logic Analysis

A Multi-Task Corpus for Assessing Discourse Coherence in Chi...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Wu, Hongyi Shen, Xinshu Lan, Man Wu, Yuanbin Bai, Xiaopeng Mao, Shaoguang East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China East China Normal Univ Shanghai Inst Educ Shanghai Peoples R China East China Normal Univ Dept Chinese Language & Literature Shanghai Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798891760608

This paper introduces the Chinese Essay Discourse Coherence Corpus (CEDCC), a multi-task dataset for assessing discourse coherence. Existing research tends to focus on isolated dimensions of discourse coherence, a gap which the CEDCC addresses by integrating coherence grading, topical continuity, and discourse relations. This approach, alongside detailed annotations, captures the subtleties of real-world texts and stimulates progress in Chinese discourse coherence analysis. Our contributions include the development of the CEDCC, the establishment of baselines for further research, and the demonstration of the impact of coherence on discourse relation recognition and automated essay scoring. The dataset and related codes is available at https://***/cubenlp/CEDCC_corpus.

关键词： Grading

来源：评论

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Prompting Large language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation

Prompting Large Language Models with Chain-of-Thought for Fe...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Liang, Yuanyuan Wang, Jianing Zhu, Hanlun Wang, Lei Qian, Weining Lan, Yunshi East China Normal Univ Shanghai Peoples R China Singapore Management Univ Singapore Singapore

ISBN: (纸本)9798891760608

The task of Question Generation over Knowledge Bases (KBQG) aims to convert a logical form into a natural language question. For the sake of expensive cost of large-scale question annotation, the methods of KBQG under low-resource scenarios urgently need to be developed. However, current methods heavily rely on annotated data for fine-tuning, which is not well-suited for few-shot question generation. The emergence of Large language Models (LLMs) has shown their impressive generalization ability in few-shot tasks. 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 split into a series of sub-question generation. Our proposed prompting method KQG-CoT first selects supportive logical forms from the unlabeled data pool taking account of the characteristics of the logical form. Then, we construct a task-specific prompt to guide LLMs to generate complicated questions based on selective logic forms. To further ensure prompt quality, we extend KQG-CoT into KQG-CoT+ via sorting the logical forms by their complexity. We conduct extensive experiments over three public KBQG datasets. The results demonstrate that our prompting method consistently outperforms other prompting baselines on the evaluated datasets. Remarkably, our KQG-CoT+ method could surpass existing few-shot SoTA results of the PathQuestions dataset by 18.25, 10.72, and 10.18 absolute points on BLEU-4, METEOR, and ROUGE-L, respectively.

关键词： Knowledge based systems

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LUQ: Long-text Uncertainty Quantification for LLMs

LUQ: Long-text Uncertainty Quantification for LLMs

引用

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

作者： Zhang, Caiqi Liu, Fangyu Basaldella, Marco Collier, Nigel Language Technology Lab University of Cambridge United Kingdom Amazon Alexa United Kingdom

ISBN: (纸本)9798891761643

Large language Models (LLMs) have demonstrated remarkable capability in a variety of NLP tasks. However, LLMs are also prone to generate nonfactual content. Uncertainty Quantification (UQ) is pivotal in enhancing our understanding of a model's confidence on its generation, thereby aiding in the mitigation of nonfactual outputs. Existing research on UQ predominantly targets short text generation, typically yielding brief, word-limited responses. However, real-world applications frequently necessitate much longer responses. Our study first highlights the limitations of current UQ methods in handling long text generation. We then introduce LUQ with its two variations: LUQ-ATOMIC and LUQ-PAIR, a series of novel sampling-based UQ approaches specifically designed for long text. Our findings reveal that LUQ outperforms existing baseline methods in correlating with the model's factuality scores (negative coefficient of -0.85 observed for Gemini Pro). To further improve the factuality of LLM responses, we propose LUQ-ENSEMBLE, a method that ensembles responses from multiple models and selects the response with the lowest uncertainty. The ensembling method greatly improves the response factuality upon the best standalone LLM. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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BLESS: Benchmarking Large language Models on Sentence Simplification

BLESS: Benchmarking Large Language Models on Sentence Simpli...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Kew, Tannon Chi, Alison Vasquez-Rodriguez, Laura Agrawal, Sweta Aumiller, Dennis Alva-Manchego, Fernando Shardlow, Matthew Univ Zurich Zurich Switzerland Natl Tsing Hua Univ Hsinchu Taiwan Idiap Res Inst Martigny Switzerland Univ Manchester Manchester Lancs England Univ Maryland College Pk MD 20742 USA Cohere San Francisco CA USA Cardiff Univ Cardiff Wales Manchester Metropolitan Univ Manchester Lancs England

ISBN: (纸本)9798891760608

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). We examine how well off-the-shelf LLMs can solve this challenging task, assessing a total of 44 models, differing in size, architecture, pre-training methods, and accessibility, on three test sets from different domains (Wikipedia, news, and medical) under a few-shot setting. Our analysis considers a suite of automatic metrics as well as a large-scale quantitative investigation into the types of common edit operations performed by the different models. Furthermore, we perform a manual qualitative analysis on a subset of model outputs to better gauge the quality of the generated simplifications. Our evaluation indicates that the best LLMs, despite not being trained on TS, perform comparably with state-of-the-art TS baselines. Additionally, we find that certain LLMs demonstrate a greater range and diversity of edit operations. Our performance benchmark will be available as a resource for the development of future TS methods and evaluation metrics.(1)

关键词： Benchmarking

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Structured Object language Modeling (SoLM): Native Structured Objects Generation Conforming to Complex Schemas with Self-Supervised Denoising

Structured Object Language Modeling (SoLM): Native Structure...

引用

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

作者： Tavanaei, Amir Koo, Kee Kiat Ceker, Hayreddin Jiang, Shaobai Li, Qi Han, Julien Bouyarmane, Karim Amazon Seattle United States

ISBN: (纸本)9798891761667

In this paper, we study the problem of generating structured objects that conform to a complex schema, with intricate dependencies between the different components (facets) of the object. The facets of the object (attributes, fields, columns, properties) can be a mix of short, structured, type-constrained facts, or long natural-language descriptions. The object has to be self-consistent between the different facets in the redundant information it carries (relative consistency), while being grounded with respect to world knowledge (absolute consistency). We frame the problem as a language Modeling problem (Structured Object language Modeling) and train an LLM to perform the task natively, without requiring instructions or prompt-engineering. We propose a self-supervised denoising method to train the model from an existing dataset of such objects. The input query can be the existing object itself, in which case the model acts as a regenerator, completing, correcting, normalizing the input, or any unstructured blurb to be structured. We show that the self-supervised denoising training provides a strong baseline, and that additional supervised fine-tuning with small amount of human demonstrations leads to further improvement. Experimental results show that the proposed method matches or outperforms prompt-engineered general-purpose state-of-the-art LLMs (Claude 3, Mixtral-8x7B), while being order-of-magnitude more cost-efficient. © 2024 Association for Computational Linguistics.

关键词： Structured Query language

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Leveraging Parameter-Efficient Fine-Tuning for Multilingual Abstractive Summarization 13th

Leveraging Parameter-Efficient Fine-Tuning for Multilingual ...

引用

13th International conference on natural language processing and Chinese Computing

作者： Shen, Jialun Wang, Yusong Tokyo Inst Technol Meguro Japan Guangdong Inst Intelligence Sci & Technol Zhuhai Peoples R China

ISBN: (纸本)9789819794362;9789819794379

In recent years, parameter-efficient fine-tuning methods have gained attention as an alternative to full fine-tuning of pre-trained language models for transfer learning. Such methods can alleviate the problem that full fine-tuning requires updating and storing all the model parameters for different tasks, showing comparable performance with much fewer tuned parameters. However, their effectiveness in the context of multilingual natural language processing remains underexplored. This paper evaluates the performance of two representative parameter-efficient fine-tuning methods, prefix-tuning and LoRA, on multilingual abstractive summarization, comparing them with traditional full fine-tuning approach. Our comprehensive analysis highlights the trade-offs between efficiency and performance, providing benchmarks to standardize evaluation in this domain. Additionally, we delve into an in-depth examination of prefix-tuning, particularly under few-shot conditions, uncovering insights into its efficacy and offering guidance for optimizing its performance in multilingual environments. Our findings contribute to a deeper understanding of the applicability and benefits of parameter-efficient fine-tuning methods in multilingual natural language processing. This research aims to inform future developments and encourage the adoption of parameter-efficient fine-tuning techniques in multilingual contexts, ultimately enhancing the performance and scalability of pre-trained language models. We have made our source code publicly available at https://***/sgallon-rin/peft-mas.

关键词： Transfer Learning Parameter-efficient Fine-tuning Multilingual Abstractive Summarization Pre-trained language Models

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Dynamic Top-k Estimation Consolidates Disagreement between Feature Attribution methods

Dynamic Top-k Estimation Consolidates Disagreement between F...

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conference on empirical methods in natural language processing (EMNLP)

作者： Kamp, Jonathan Beinborn, Lisa Fokkens, Antske Vrije Univ Amsterdam Computat Linguist & Text Min Lab Amsterdam Netherlands Eindhoven Univ Technol Dept Math & Comp Sci Eindhoven Netherlands

ISBN: (纸本)9798891760608

Feature attribution scores are used for explaining the prediction of a text classifier to users by highlighting a k number of tokens. 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. Our approach is dynamic across sentences, method-agnostic, and deals with sentence length bias. We compare agreement between multiple methods and humans on an NLI task, using fixed k and dynamic k. We find that perturbation-based methods and Vanilla Gradient exhibit highest agreement on most method-method and method-human agreement metrics with a static k. Their advantage over other methods disappears with dynamic ks which mainly improve Integrated Gradient and GradientXInput. To our knowledge, this is the first evidence that sequential properties of attribution scores are informative for consolidating attribution signals for human interpretation.

关键词： Perturbation techniques

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CogGPT: Unleashing the Power of Cognitive Dynamics on Large language Models

CogGPT: Unleashing the Power of Cognitive Dynamics on Large ...

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

作者： Lv, Yaojia Pan, Haojie Wang, Zekun Liang, Jiafeng Liu, Yuanxing Fu, Ruiji Liu, Ming Wang, Zhongyuan Qin, Bing Harbin Institute of Technology China Peng Cheng Laboratory China Kuaishou Inc. China

ISBN: (纸本)9798891761681

Cognitive dynamics, which refer to the evolution in human cognitive processes, are pivotal to advance human understanding of the world. Recent advancements in large language models (LLMs) highlight their potential for cognitive simulation. However, these LLM-based cognitive studies primarily focus on replicating human cognition in specific contexts, overlooking the inherently dynamic nature of cognition. To bridge this gap, we explore the cognitive dynamics of LLMs and present a corresponding task inspired by longitudinal studies. Toward the task, we develop CogBench, a novel benchmark to assess the cognitive dynamics of LLMs and validate it through participant surveys. We also design two evaluation metrics for CogBench, including Authenticity and Rationality. Recognizing the inherent static nature of LLMs, we further introduce CogGPT for the task, which features an innovative iterative cognitive mechanism to develop lifelong cognitive dynamics. empirical results demonstrate the superiority of CogGPT over several existing methods, particularly in its ability to facilitate role-specific cognitive dynamics under continuous information flows. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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