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

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Attribute or Abstain: Large language Models as Long Document Assistants

Attribute or Abstain: Large Language Models as Long Document...

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

作者： Buchmann, Jan Liu, Xiao Gurevych, Iryna Technical University of Darmstadt Germany

ISBN: (纸本)9798891761643

LLMs can help humans working with long documents, but are known to hallucinate. Attribution can increase trust in LLM responses: The LLM provides evidence that supports its response, which enhances verifiability. Existing approaches to attribution have only been evaluated in RAG settings, where the initial retrieval confounds LLM performance. This is crucially different from the long document setting, where retrieval is not needed, but could help. Thus, a long document specific evaluation of attribution is missing. To fill this gap, we present LAB, a benchmark of 6 diverse long document tasks with attribution, and experiments with different approaches to attribution on 5 LLMs of different sizes. We find that citation, i.e. response generation and evidence extraction in one step, performs best for large and fine-tuned models, while additional retrieval can help for small, prompted models. We investigate whether the "Lost in the Middle" phenomenon exists for attribution, but do not find this. We also find that evidence quality can predict response quality on datasets with simple responses, but not so for complex responses, as models struggle with providing evidence for complex claims. We release code and data for further investigation. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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ADT: An Additive Delta-Tuning approach for parameter-efficient tuning in pre-trained language models 6

ADT: An Additive Delta-Tuning approach for parameter-efficie...

引用

6th International conference on natural language processing (ICNLP)

作者： Li, Dong Tang, Jintao Li, Shasha Wang, Ting Natl Univ Def Technol Coll Comp Sci & Technol Changsha Peoples R China

ISBN: (纸本)9798350349122;9798350349115

The advent of pre-trained language models (PLMs) has revolutionized the field of natural language processing (NLP), enabling models to leverage vast amounts of language and world knowledge for various downstream tasks. However, the conventional fine-tuning of these models requires updating full parameters, which is computationally intensive and impractical in resource-constrained settings. Parameter-efficient tuning methods have been developed to mitigate this. However, these methods may need to be improved in terms of inference latency, optimization difficulty, and input sequence length reduction. In this paper, we introduce a new reparameterization-based parameter-efficient tuning method, Additive Delta Tuning (ADT). ADT only fine-tunes part of the row and column parameters in the PLM weight matrix. This approach significantly reduces the computational cost compared to existing methods, such as Low-Rank Adaptation (LoRA), while maintaining competitive performance. We conducted extensive experiments across multiple natural language understanding (NLU) tasks, including text classification, text entailment, named entity recognition, etc., to evaluate the effectiveness of ADT. Our experimental results demonstrate that ADT achieves comparable or better performance than traditional fine-tuning methods, requiring less than 0.3% parameter updates. This shows that ADT is an effective method for parameter-efficient tuning. Our method achieves comparable results to LoRA on multiple NLU tasks, with the added benefit of requiring less computational cost.

关键词： Parameter-Efficient Tuning Pre-trained language Models Additive Delta-Tuning LoRA

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Liar, Liar, Logical Mire: A Benchmark for Suppositional Reasoning in Large language Models

Liar, Liar, Logical Mire: A Benchmark for Suppositional Reas...

引用

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

作者： Mondorf, Philipp Plank, Barbara MaiNLP Center for Information and Language Processing LMU Munich Germany Munich Germany

ISBN: (纸本)9798891761643

Knights and knaves problems represent a classic genre of logical puzzles where characters either tell the truth or lie. The objective is to logically deduce each character's identity based on their statements. The challenge arises from the truth-telling or lying behavior, which influences the logical implications of each statement. Solving these puzzles requires not only direct deductions from individual statements, but the ability to assess the truthfulness of statements by reasoning through various hypothetical scenarios. As such, knights and knaves puzzles serve as compelling examples of suppositional reasoning. In this paper, we introduce TruthQuest, a benchmark for suppositional reasoning based on the principles of knights and knaves puzzles. Our benchmark presents problems of varying complexity, considering both the number of characters and the types of logical statements involved. Evaluations on TruthQuest show that large language models like Llama 3 and Mixtral-8x7B exhibit significant difficulties solving these tasks. A detailed error analysis of the models' output reveals that lower-performing models exhibit a diverse range of reasoning errors, frequently failing to grasp the concept of truth and lies. In comparison, more proficient models primarily struggle with accurately inferring the logical implications of potentially false statements. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Re-ReST: Reflection-Reinforced Self-Training for language Agents

Re-ReST: Reflection-Reinforced Self-Training for Language Ag...

引用

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

作者： Dou, Zi-Yi Yang, Cheng-Fu Wu, Xueqing Chang, Kai-Wei Peng, Nanyun University of California Los Angeles United States

ISBN: (纸本)9798891761643

Finetuning language agents with reasoning-action trajectories is effective, but obtaining these trajectories from human annotations or stronger models is costly and sometimes impractical. In this paper, we investigate the use of self-training in language agents, which can generate supervision from the agent itself, offering a promising alternative without relying on human or stronger model demonstrations. Self-training, however, requires high-quality model-generated samples, which are hard to obtain for challenging language agent tasks. To address this, we present Reflection-Reinforced Self-Training (Re-ReST), which uses a reflector to refine low-quality generated samples during self-training. The reflector takes the agent's output and feedback from an external environment (e.g., unit test results in code generation) to produce improved samples. This technique enhances the quality of inferior samples and efficiently enriches the self-training dataset with higher-quality samples. We conduct extensive experiments on open-source language agents across tasks, including multi-hop question answering, sequential decision-making, code generation, visual question answering, and text-to-image generation. The results demonstrate the effectiveness of self-training and Re-ReST in language agent tasks, with self-training improving baselines by 7.6% on HotpotQA and 28.4% on AlfWorld, and Re-ReST further boosting performance by 2.0% and 14.1%, respectively. Our studies also confirm the efficiency of using a reflector to generate high-quality samples for self-training. Moreover, we demonstrate a method to employ reflection during inference without ground-truth feedback, addressing the limitation of previous reflection work. Our code is released at https://***/PlusLabNLP/Re-ReST. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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VIP5: Towards Multimodal Foundation Models for Recommendation

VIP5: Towards Multimodal Foundation Models for Recommendatio...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Geng, Shijie Tan, Juntao Liu, Shuchang Fu, Zuohui Zhang, Yongfeng Rutgers State Univ Dept Comp Sci Piscataway NJ 08854 USA

ISBN: (纸本)9798891760615

Computer Vision (CV), natural language processing (NLP), and Recommender Systems (RecSys) are three prominent AI applications that have traditionally developed independently, resulting in disparate modeling and engineering methodologies. This has impeded the ability for these fields to directly benefit from each other's advancements. With the recent development of foundation models, large language models have emerged as a potential generalpurpose interface for unifying different modalities and problem formulations. In light of this, we propose the development of a multimodal foundation model (MFM) considering visual, textual, and personalization modalities under the P5 recommendation paradigm, thus named VIP5 (Visual P5), to unify various modalities and recommendation tasks. This will enable the processing of multiple modalities in a shared architecture for improved recommendations. To achieve this, we introduce multimodal personalized prompts to accommodate multiple modalities under a shared format. Additionally, we propose a parameter-efficient training method for foundation models, which involves freezing the P5 backbone and fine-tuning lightweight adapters, resulting in improved recommendation performance and increased efficiency in terms of training time and memory usage. Code and data of VIP5 are available at https:// ***/jeykigung/VIP5.

关键词： natural language processing systems

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Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large language Models

Multi-LogiEval: Towards Evaluating Multi-Step Logical Reason...

引用

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

作者： Patel, Nisarg Kulkarni, Mohith Parmar, Mihir Budhiraja, Aashna Nakamura, Mutsumi Varshney, Neeraj Baral, Chitta Arizona State University United States

ISBN: (纸本)9798891761643

As Large language Models (LLMs) continue to exhibit remarkable performance in natural language understanding tasks, there is a crucial need to measure their ability for human-like multi-step logical reasoning. Existing logical reasoning evaluation benchmarks often focus primarily on simplistic single-step or multi-step reasoning with a limited set of inference rules. Furthermore, the lack of datasets for evaluating non-monotonic reasoning represents a crucial gap since it aligns more closely with human-like reasoning. To address these limitations, we propose Multi-LogiEval, a comprehensive evaluation dataset encompassing multi-step logical reasoning with various inference rules and depths. Multi-LogiEval covers three logic types - propositional, first-order, and non-monotonic consisting of more than 30 inference rules and more than 60 of their combinations with various depths. Leveraging this dataset, we conduct evaluations on a range of LLMs including GPT-4, ChatGPT, Gemini-Pro, Yi, Orca, and Mistral, employing a zero-shot chain-of-thought. Experimental results show that there is a significant drop in the performance of LLMs as the reasoning steps/depth increases (average accuracy of ∼ 68% at depth-1 to ∼ 43% at depth-5). We further conduct a thorough investigation of reasoning chains generated by LLMs which reveals several important findings. We believe that Multi-LogiEval facilitates future research for evaluating and enhancing the logical reasoning ability of LLMs. © 2024 Association for Computational Linguistics.

关键词： Chains

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Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs

Enhancing Legal Case Retrieval via Scaling High-quality Synt...

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

作者： Gao, Cheng Xiao, Chaojun Liu, Zhenghao Chen, Huimin Liu, Zhiyuan Sun, Maosong NLP Group DCST IAI BNRIST Tsinghua University Beijing China Quan Cheng Laboratory China Northeastern University China School of Journalism and Communication Tsinghua University China

ISBN: (纸本)9798891761643

Legal case retrieval (LCR) aims to provide similar cases as references for a given fact description. This task is crucial for promoting consistent judgments in similar cases, effectively enhancing judicial fairness and improving work efficiency for judges. However, existing works face two main challenges for real-world applications: existing works mainly focus on case-to-case retrieval using lengthy queries, which does not match real-world scenarios;and the limited data scale, with current datasets containing only hundreds of queries, is insufficient to satisfy the training requirements of existing data-hungry neural models. To address these issues, we introduce an automated method to construct synthetic query-candidate pairs and build the largest LCR dataset to date, LEAD, which is hundreds of times larger than existing datasets. This data construction method can provide ample training signals for LCR models. Experimental results demonstrate that model training with our constructed data can achieve state-of-the-art results on two widely-used LCR benchmarks. Besides, the construction method can also be applied to civil cases and achieve promising results. The data and codes can be found in https://***/thunlp/LEAD. © 2024 Association for Computational Linguistics.

关键词： Structured Query language

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Exploring the Potential of Prompting methods in Low-Resource Speech Recognition with Whisper 13th

Exploring the Potential of Prompting Methods in Low-Resource...

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13th International conference on natural language processing and Chinese Computing

作者： Chen, Yaqi Zhang, Wenlin Zhang, Hao Yang, Xukui Qu, Dan Informat Engn Univ Sch Informat Syst Engn Zhengzhou Peoples R China Lab Adv Comp & Intelligence Engn Wuxi Jiangsu Peoples R China

ISBN: (纸本)9789819794362;9789819794379

Recent advancements in large-scale pre-trained automatic speech recognition (ASR) foundation models (e.g., Whisper) have exhibited remarkable performance in speech processing tasks. But fine-tuning such models for low-resource languages can be computationally expensive and prone to overfitting. Prompting methods offer a solution by designing specific prompts in the inputs that guide the model's behavior for targeted tasks, facilitating parameter-efficient adaptation. This paper presents the first exploration of various prompt tuning methods and optimized strategies for low-resource ASR based on Whisper. Moreover, we propose a shallow integration method to utilize the advantage of deep prompt tuning and reparametrization. Extensive experiments on the Common Voice and FLEURS datasets show the competitive performance of prompt tuning compared to full fine-tuning and Lora with fewer trainable parameters. Notably, the shallow integration strategy yields impressive results, especially for small models.

关键词： low-resource automatic speech recognition Whisper prompt tuning

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Model Perturbation-based Privacy Attacks on language Models

Model Perturbation-based Privacy Attacks on Language Models

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2023 conference on empirical methods in natural language processing, EMNLP 2023

作者： Li, Marvin Wang, Jason Wang, Jeffrey Neel, Seth Harvard College United States

ISBN: (纸本)9798891760608

Recent work has shown that Large language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present MoPeθ (Model Perturbations), 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 MoPeθ adds noise to the model in parameter space and measures the drop in log-likelihood at a given point x, a statistic we show approximates the trace of the Hessian matrix with respect to model parameters. Across language models ranging from 70M to 12B parameters, we show that MoPeθ is more effective than existing loss-based attacks and recently proposed perturbation-based methods We also examine the role of training point order and model size in attack success, and empirically demonstrate that MoPeθ accurately approximate the trace of the Hessian in practice Our results show that the loss of a point alone is insufficient to determine extractability-there are training points we can recover using our method that have average loss. This casts some doubt on prior works that use the loss of a point as evidence of memorization or "unlearning. ©2023 Association for Computational Linguistics.

关键词： Computational linguistics

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On the Independence of Association Bias and empirical Fairness in language Models 23

On the Independence of Association Bias and Empirical Fairne...

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6th ACM conference on Fairness, Accountability, and Transparency (FAccT)

作者： Cabello, Laura Jorgensen, Anna Katrine Sogaard, Anders Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9781450372527

The societal impact of pre-trained language models has prompted researchers to probe them for strong associations between protected attributes and value-loaded terms, from slur to prestigious job titles. Such work is said to probe models for bias or fairness-or such probes 'into representational biases' are said to be 'motivated by fairness'-suggesting an intimate connection between bias and fairness. We provide conceptual clarity by distinguishing between association biases [11] and empirical fairness [56] and show the two can be independent. Our main contribution, however, is showing why this should not come as a surprise. To this end, we first provide a thought experiment, showing how association bias and empirical fairness can be completely orthogonal. Next, we provide empirical evidence that there is no correlation between bias metrics and fairness metrics across the most widely used language models. Finally, we survey the sociological and psychological literature and show how this literature provides ample support for expecting these metrics to be uncorrelated.

关键词： Representational Bias Fairness natural language processing

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