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

共 15363 条记录，以下是891-900 订阅

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Auto-Intent: Automated Intent Discovery and Self-Exploration for Large language Model Web Agents

Auto-Intent: Automated Intent Discovery and Self-Exploration...

引用

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

作者： Kim, Jaekyeom Kim, Dong-Ki Logeswaran, Lajanugen Sohn, Sungryull Lee, Honglak LG AI Research Korea Republic of Field AI United States University of Michigan United States

ISBN: (纸本)9798891761681

In this paper, we introduce Auto-Intent, a method to adapt a pre-trained large language model (LLM) as an agent for a target domain without direct fine-tuning, where we empirically focus on web navigation tasks. Our approach first discovers the underlying intents from target domain demonstrations unsupervisedly, in a highly compact form (up to three words). With the extracted intents, we train our intent predictor to predict the next intent given the agent's past observations and actions. In particular, we propose a self-exploration approach where top-k probable intent predictions are provided as a hint to the pre-trained LLM agent, which leads to enhanced decision-making capabilities. Auto-Intent substantially improves the performance of GPT-{3.5, 4} and Llama-3.1-{70B, 405B} agents on the large-scale real-website navigation benchmarks from Mind2Web and online navigation tasks from WebArena with its cross-benchmark generalization from Mind2Web. © 2024 Association for Computational Linguistics.

关键词： Decision making

来源：评论

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Predict and Use: Harnessing Predicted Gaze to Improve Multimodal Sarcasm Detection

Predict and Use: Harnessing Predicted Gaze to Improve Multim...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Tiwari, Divyank Pratap Kanojia, Diptesh Ray, Anupama Nunna, Apoorva Bhattacharyya, Pushpak Indian Inst Technol Comp Indian Language Technol Mumbai Maharashtra India Univ Surrey Surrey Inst People Centred AI Guildford Surrey England IBM Res India Bangalore Karnataka India

ISBN: (纸本)9798891760608

Sarcasm is a complex linguistic construct with incongruity at its very core. Detecting sarcasm depends on the actual content spoken and tonality, facial expressions, the context of an utterance, and personal traits like language proficiency and cognitive capabilities. In this paper, we propose the utilization of synthetic gaze data to improve the task performance for multimodal sarcasm detection in a conversational setting. We enrich an existing multimodal conversational dataset, i.e., MUStARD++ with gaze features. With the help of human participants, we collect gaze features for < 20% of data instances, and we investigate various methods for gaze feature prediction for the rest of the dataset. We perform extrinsic and intrinsic evaluations to assess the quality of the predicted gaze features. We observe a performance gain of up to 6.6% points by adding a new modality, i.e., collected gaze features. When both collected and predicted data are used, we observe a performance gain of 2.3% points on the complete dataset. Interestingly, with only predicted gaze features, too, we observe a gain in performance (1.9% points). We retain and use the feature prediction model, which maximally correlates with collected gaze features. Our model trained on combining collected and synthetic gaze data achieves SoTA performance on the MUStARD++ dataset. To the best of our knowledge, ours is the first predict-and-use model for sarcasm detection. We publicly release(1) the code, gaze data, and our best models for further research.

关键词： Forecasting

来源：评论

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SELFCHECKGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large language Models

SELFCHECKGPT: Zero-Resource Black-Box Hallucination Detectio...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Manakul, Potsawee Liusie, Adian Gales, Mark J. F. Univ Cambridge ALTA Inst Dept Engn Cambridge England

ISBN: (纸本)9798891760608

Generative Large language Models (LLMs) such as GPT-3 are capable of generating highly fluent responses to a wide variety of user prompts. However, LLMs are known to hallucinate facts and make non-factual statements which can undermine trust in their output. Existing fact-checking approaches either require access to the output probability distribution (which may not be available for systems such as ChatGPT) or external databases that are interfaced via separate, often complex, modules. 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. SelfCheckGPT leverages the simple idea that if an LLM has knowledge of a given concept, sampled responses are likely to be similar and contain consistent facts. However, for hallucinated facts, stochastically sampled responses are likely to diverge and contradict one another. We investigate this approach by using GPT-3 to generate passages about individuals from theWikiBio dataset, and manually annotate the factuality of the generated passages. We demonstrate that SelfCheckGPT can: i) detect non-factual and factual sentences;and ii) rank passages in terms of factuality. We compare our approach to several baselines and show that our approach has considerably higher AUC-PR scores in sentence-level hallucination detection and higher correlation scores in passage-level factuality assessment compared to grey-box methods.

关键词： Probability distributions

来源：评论

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Large language Models Can Self-Correct with Key Condition Verification

Large Language Models Can Self-Correct with Key Condition Ve...

引用

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

作者： Wu, Zhenyu Zeng, Qingkai Zhang, Zhihan Tan, Zhaoxuan Shen, Chao Jiang, Meng Xi'an Jiaotong University China University of Notre Dame United States

ISBN: (纸本)9798891761643

Intrinsic self-correct was a method that instructed large language models (LLMs) to verify and correct their responses without external feedback. Unfortunately, the study concluded that the LLMs could not self-correct reasoning yet. We find that a simple yet effective prompting method enhances LLM performance in identifying and correcting inaccurate answers without external feedback. That is to mask a key condition in the question, add the current response to construct a verification question, and predict the condition to verify the response. The condition can be an entity in an open-domain question or a numerical value in an arithmetic question, which requires minimal effort (via prompting) to identify. We propose an iterative verify-then-correct framework to progressively identify and correct (probably) false responses, named PROCO. We conduct experiments on three reasoning tasks. On average, PROCO, with GPT-3.5-Turbo-1106 as the backend LLM, yields +6.8 exact match on four open-domain question answering datasets, +14.1 accuracy on three arithmetic reasoning datasets, and +9.6 accuracy on a commonsense reasoning dataset, compared to Self-Correct. Our implementation is made publicly available at https://***/***/. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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To Preserve or To Compress: An In-Depth Study of Connector Selection in Multimodal Large language Models

To Preserve or To Compress: An In-Depth Study of Connector S...

引用

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

作者： Lin, Junyan Chen, Haoran Zhu, Dawei Shen, Xiaoyu Digital Twin Institute Eastern Institute of Technology Ningbo China Saarland University Saarland Informatics Campus Germany

ISBN: (纸本)9798891761643

In recent years, multimodal large language models (MLLMs) have garnered significant attention from both industry and academia. However, there is still considerable debate on constructing MLLM architectures, particularly regarding the selection of appropriate connectors for perception tasks of varying granularities. This paper systematically investigates the impact of connectors on MLLM performance. Specifically, we classify connectors into feature-preserving and feature-compressing types. Utilizing a unified classification standard, we categorize sub-tasks from three comprehensive benchmarks, MMBench, MME, and SEED-Bench, into three task types: coarse-grained perception, fine-grained perception, and reasoning, and evaluate the performance. Our findings reveal that feature-preserving connectors excel in fine-grained perception tasks due to their ability to retain detailed visual information. In contrast, feature-compressing connectors, while less effective in fine-grained perception tasks, offer significant speed advantages and perform comparably in coarse-grained perception and reasoning tasks. These insights are crucial for guiding MLLM architecture design and advancing the optimization of MLLM architectures. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Learning to Extract Structured Entities Using language Models

Learning to Extract Structured Entities Using Language Model...

引用

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

作者： Wu, Haolun Yuan, Ye Mikaelyan, Liana Meulemans, Alexander Liu, Xue Hensman, James Mitra, Bhaskar McGill University Canada Mila - Quebec AI Institute Canada Microsoft Research United States ETH Zürich Switzerland

ISBN: (纸本)9798891761643

Recent advances in machine learning have significantly impacted the field of information extraction, with language Models (LMs) playing a pivotal role in extracting structured information from unstructured text. Prior works typically represent information extraction as triplet-centric and use classical metrics such as precision and recall for evaluation. We reformulate the task to be entity-centric, enabling the use of diverse metrics that can provide more insights from various perspectives. We contribute to the field by introducing Structured Entity Extraction and proposing the Approximate Entity Set OverlaP (AESOP) metric, designed to appropriately assess model performance. Later, we introduce a new Multistage Structured Entity Extraction (MuSEE) model that harnesses the power of LMs for enhanced effectiveness and efficiency by decomposing the extraction task into multiple stages. Quantitative and human side-by-side evaluations confirm that our model outperforms baselines, offering promising directions for future advancements in structured entity extraction. Our source code is available at https://***/microsoft/Structured-Entity-Extraction. © 2024 Association for Computational Linguistics.

关键词： Information retrieval

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Mixed Distillation Helps Smaller language Models Reason Better

Mixed Distillation Helps Smaller Language Models Reason Bett...

引用

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

作者： Li, Chenglin Chen, Qianglong Li, Liangyue Wang, Caiyu Tao, Feng Li, Yicheng Chen, Zulong Zhang, Yin Zhejiang University Hangzhou China Dalian Medical University Dalian China Alibaba Hangzhou China

ISBN: (纸本)9798891761681

As large language models (LLMs) have demonstrated impressive multiple step-by-step reasoning capabilities in recent natural language processing (NLP) reasoning tasks, many studies are interested in distilling reasoning abilities into smaller language models (SLMs) via fine-tuning. Previous distillation methods usually utilize the capabilities of LLMs to generate chain-of-thought (CoT) samples to teach SLMs. However, this distillation approach performs poorly in certain scenarios due to the limitations of CoT. In this work, we introduce a novel Mixed Distillation (MD) framework, distilling multiple step-by-step reasoning abilities into SLMs. First, we leverage LLMs to generate multiple step-by-step reasoning rationales by sampling automatically. Then, we create high-quality, well-balanced mixed thought data and design a novel multi-task loss to help SLMs better learn and adaptively activate multiple step-by-step reasoning. Our extensive experiments demonstrate that MD enhances both single-path (using either CoT or PoT) and multi-path (using both CoT and PoT) reasoning abilities of SLMs during inference across reasoning tasks. Notably, a single model generated by MD exceeds the comprehensive performance of an ensemble of two individual CoT and PoT distilled models. Mistral-7B using MD can achieve remarkable improvements of 87.5%, 74.0% and 77.1% on SVAMP, GSM8K and ASDIV, respectively, outperforming the teacher model, GPT-3.5-Turbo. We hope our work provides insight into SLMs' multiple step-by-step reasoning abilities. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Revisiting Catastrophic Forgetting in Large language Model Tuning

Revisiting Catastrophic Forgetting in Large Language Model T...

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

作者： Li, Hongyu Ding, Liang Fang, Meng Tao, Dacheng Wuhan University China University of Liverpool United Kingdom The University of Sydney Australia Nanyang Technological University Singapore

ISBN: (纸本)9798891761681

Catastrophic Forgetting (CF) means models forgetting previously acquired knowledge when learning new data. It compromises the effectiveness of large language models (LLMs) during fine-tuning, yet the underlying causes have not been thoroughly investigated. This paper takes the first step to reveal the direct link between the flatness of the model loss landscape and the extent of CF in the field of LLMs. Based on this, we introduce the sharpness-aware minimization to mitigate CF by flattening the loss landscape. Experiments on three widely-used fine-tuning datasets, spanning different model scales, demonstrate the effectiveness of our method in alleviating CF. Analyses show that we nicely complement the existing anti-forgetting strategies, further enhancing the resistance of LLMs to CF. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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MetaGPT: Merging Large language Models Using Model Exclusive Task Arithmetic

MetaGPT: Merging Large Language Models Using Model Exclusive...

引用

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

作者： Zhou, Yuyan Song, Liang Wang, Bingning Chen, Weipeng Baichuan Inc. China

ISBN: (纸本)9798891761643

The advent of large language models (LLMs) like GPT-4 has catalyzed the exploration of multi-task learning (MTL), in which a single model demonstrates proficiency across diverse tasks. Task arithmetic has emerged as a cost-effective approach for MTL. It enables performance enhancement across multiple tasks by adding their corresponding task vectors to a pre-trained model. However, the current lack of a method that can achieve optimal performance with low computational cost and protecting the data privacy, which limits their application to LLMs. In this paper, we propose Model Exclusive Task Arithmetic for merging GPT-scale models (MetaGPT), which formalizes the objective of model merging into a multi-task learning framework, aiming to minimize the average loss difference between the merged model and each individual task model. Since data privacy limits the use of multi-task training data, we leverage LLMs' local linearity and task vectors' orthogonality to separate the data term and scaling coefficients term and derive a model-exclusive task arithmetic method. Our proposed MetaGPT is data-agnostic and bypasses the heavy search process, making it cost-effective and easy to implement for LLMs. Extensive experiments demonstrate that MetaGPT leads to improvements in task arithmetic and achieves state-of-the-art performance on multiple tasks. © 2024 Association for Computational Linguistics.

关键词： Multi-task learning

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Visual Question Decomposition on Multimodal Large language Models

Visual Question Decomposition on Multimodal Large Language M...

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

作者： Zhang, Haowei Liu, Jianzhe Han, Zhen Chen, Shuo He, Bailan Tresp, Volker Xu, Zhiqiang Gu, Jindong Technical University of Munich Germany Amazon Web Services United States LMU Munich Germany Munich Center for Machine Learning Germany MBZUAI United Arab Emirates University of Oxford United Kingdom

ISBN: (纸本)9798891761681

Question decomposition has emerged as an effective strategy for prompting Large language Models (LLMs) to answer complex questions. However, while existing methods primarily focus on unimodal language models, the question decomposition capability of Multimodal Large language Models (MLLMs) has yet to be explored. To this end, this paper explores visual question decomposition on MLLMs. Specifically, we introduce a systematic evaluation framework including a dataset and several evaluation criteria to assess the quality of the decomposed sub-questions, revealing that existing MLLMs struggle to produce high-quality sub-questions. To address this limitation, we propose a specific finetuning dataset, DecoVQA+, for enhancing the model's question decomposition capability. Aiming at enabling models to perform appropriate selective decomposition, we propose an efficient finetuning pipeline. The finetuning pipeline consists of our proposed dataset and a training objective for selective decomposition. Finetuned MLLMs demonstrate significant improvements in the quality of sub-questions and the policy of selective question decomposition. Additionally, the models also achieve higher accuracy with selective decomposition on VQA benchmark datasets. © 2024 Association for Computational Linguistics.

关键词： Question answering

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