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

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HISTALIGN: Improving Context Dependency in language Generation by Aligning with History

HISTALIGN: Improving Context Dependency in Language Generati...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Wan, David Zhang, Shiyue Bansal, Mohit UNC Chapel Hill Chapel Hill NC 27599 USA

ISBN: (纸本)9798891760608

language models (LMs) can generate hallucinations and incoherent outputs, which highlights their weak context dependency. CacheLMs, which augment LMs with a memory of recent history, can increase context dependency and have shown remarkable performance in diverse language generation tasks. 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. We first prove our concept on a simple and synthetic task where the memory is essential for correct predictions, and we show that the cache component of HISTALIGN is better aligned and improves overall performance. Next, we evaluate HISTALIGN on diverse downstream language generation tasks, including prompt continuation, abstractive summarization, and data-to-text. We demonstrate that HISTALIGN improves text coherence and faithfulness in open-ended and conditional generation settings, respectively. HISTALIGN is also generalizable across different model families, showcasing its strength in improving context dependency of LMs in diverse scenarios.(1)

关键词： Computational linguistics

来源：评论

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Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition

Prompt-based Logical Semantics Enhancement for Implicit Disc...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Wang, Chenxu Jian, Ping Huang, Mu Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Beijing Inst Technol Beijing Engn Res Ctr High Volume Language Informa Beijing Peoples R China

ISBN: (纸本)9798891760608

Implicit Discourse Relation Recognition (IDRR), which infers discourse relations without the help of explicit connectives, is still a crucial and challenging task for discourse parsing. Recent works tend to exploit the hierarchical structure information from the annotated senses, which demonstrate enhanced discourse relation representations can be obtained by integrating sense hierarchy. Nevertheless, the performance and robustness for IDRR are significantly constrained by the availability of annotated data. 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. Essentially, our method seamlessly injects knowledge relevant to discourse relation into pre-trained language models through prompt-based connective prediction. Furthermore, considering the prompt-based connective prediction exhibits local dependencies due to the deficiency of masked language model (MLM) in capturing global semantics, we design a novel self-supervised learning objective based on mutual information maximization to derive enhanced representations of logical semantics for IDRR. Experimental results on PDTB 2.0 and CoNLL16 datasets demonstrate that our method achieves outstanding and consistent performance against the current state-of-the-art models.(1)

关键词： Semantics

来源：评论

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DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller language Models

DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Han, Chengcheng Du, Xiaowei Zhang, Che Lian, Yixin Li, Xiang Gao, Ming Wang, Baoyuan East China Normal Univ Sch Data Sci & Engn Shanghai Peoples R China Xiaobing AI Boston MA 02199 USA Peking Univ Sch Software & Microelect Beijing Peoples R China East China Normal Univ KLATASDS MOE Sch Stat Shanghai Peoples R China

ISBN: (纸本)9798891760608

Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large language Models (LLMs) with at least 100 billion parameters. However, it is ineffective or even detrimental when applied to reasoning tasks in Smaller language Models (SLMs) with less than 10 billion parameters. To address this limitation, we introduce Dialogue-guided Chain-of-Thought (DialCoT) which employs a dialogue format to generate intermediate reasoning steps, guiding the model toward the final answer. Additionally, we optimize the model's reasoning path selection using the Proximal Policy Optimization (PPO) algorithm, further enhancing its reasoning capabilities. Our method offers several advantages compared to previous approaches. Firstly, we transform the process of solving complex reasoning questions by breaking them down into a series of simpler sub-questions, significantly reducing the task difficulty and making it more suitable for SLMs. Secondly, we optimize the model's reasoning path selection through the PPO algorithm. We conduct comprehensive experiments on four arithmetic reasoning datasets, demonstrating that our method achieves significant performance improvements compared to state-of-the-art competitors.(1)

关键词： Computational linguistics

来源：评论

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Can Transformers Learn n-gram language Models?

Can Transformers Learn n-gram Language Models?

引用

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

作者： Svete, Anej Borenstein, Nadav Zhou, Mike Augenstein, Isabelle Cotterell, Ryan ETH Zürich Switzerland University of Copenhagen Denmark University of Pennsylvania United States

ISBN: (纸本)9798891761643

Much theoretical work has described the ability of transformers to represent formal languages. However, linking theoretical results to empirical performance is not straightforward due to the complex interplay between the architecture, the learning algorithm, and training data. To test whether theoretical lower bounds imply learnability of formal languages, we turn to recent work relating transformers to n-gram language models (LMs). We study transformers' ability to learn random n-gram LMs of two kinds: ones with arbitrary next-symbol probabilities and ones where those are defined with shared parameters. We find that classic estimation techniques for n-gram LMs such as add-λ smoothing outperform transformers on the former, while transformers perform better on the latter, outperforming methods specifically designed to learn n-gram LMs. ***/rycolab/learning-ngrams. © 2024 Association for Computational Linguistics.

关键词： Distribution transformers

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Beyond Factuality: A Comprehensive Evaluation of Large language Models as Knowledge Generators

Beyond Factuality: A Comprehensive Evaluation of Large Langu...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Chen, Liang Deng, Yang Bian, Yatao Qin, Zeyu Wu, Bingzhe Chua, Tat-Seng Wong, Kam-Fai Chinese Univ Hong Kong Hong Kong Peoples R China Tencent AI Lab Shenzhen Peoples R China Natl Univ Singapore Singapore Singapore Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798891760608

Large language models (LLMs) outperform information retrieval techniques for downstream knowledge-intensive tasks when being prompted to generate world knowledge. Yet, 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. We conduct an extensive empirical analysis of the generated knowledge from three different types of LLMs on two widely-studied knowledge-intensive tasks, i.e., open-domain question answering and knowledge-grounded dialogue. Surprisingly, our study reveals that the factuality of generated knowledge, even if lower, does not significantly hinder downstream tasks. Instead, the relevance and coherence of the outputs are more important than small factual mistakes. Further, we show how to use CONNER to improve knowledge-intensive tasks by designing two strategies: Prompt Engineering and Knowledge Selection. Our evaluation code and LLM-generated knowledge with human annotations will be released(1) to facilitate future research.

关键词： Computational linguistics

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Large language Models are Temporal and Causal Reasoners for Video Question Answering

Large Language Models are Temporal and Causal Reasoners for ...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Ko, Dohwan Lee, Ji Soo Kang, Wooyoung Roh, Byungseok Kim, Hyunwoo J. Korea Univ Dept Comp Sci & Engn Seoul South Korea Kakao Brain Jeju City South Korea

ISBN: (纸本)9798891760608

Large language Models (LLMs) have shown remarkable performances on a wide range of natural language understanding and generation tasks. We observe that the LLMs provide effective priors in exploiting linguistic shortcuts for temporal and causal reasoning in Video Question Answering (VideoQA). However, such priors often cause suboptimal results on VideoQA by leading the model to overrely on questions, i.e., linguistic bias, while ignoring visual content. This is also known as 'ungrounded guesses' or 'hallucinations'. To address this problem while leveraging LLMs' prior on VideoQA, we propose a novel framework, Flipped-VQA, encouraging the model to predict all the combinations of.V, Q, A. triplet by flipping the source pair and the target label to understand their complex relationships, i.e., predict A, Q, and V given a VQ, VA, and QA pairs, respectively. 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. Furthermore, our Flipped-VQA is a general framework that is applicable to various LLMs (OPT and GPT-J) and consistently improves their performances. We empirically demonstrate that Flipped-VQA not only enhances the exploitation of linguistic shortcuts but also mitigates the linguistic bias, which causes incorrect answers over-relying on the question. Code is available at https://***/mlvlab/Flipped-VQA.

关键词： Computational linguistics

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On the empirical Complexity of Reasoning and Planning in LLMs

On the Empirical Complexity of Reasoning and Planning in LLM...

引用

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

作者： Kang, Liwei Zhao, Zirui Hsu, David Lee, Wee Sun National University of Singapore Singapore

ISBN: (纸本)9798891761681

Chain-of-thought (CoT), tree-of-thought (ToT), and related techniques work surprisingly well in practice for some complex reasoning tasks with Large language Models (LLMs), but why? This work seeks the underlying reasons by conducting experimental case studies and linking the performance benefits to well-established sample and computational complexity principles in machine learning. We experimented with 6 reasoning tasks, ranging from grade school math, air travel planning, ..., to Blocksworld. The results suggest that (i) both CoT and ToT benefit significantly from task decomposition, which breaks a complex reasoning task into a sequence of steps with low sample complexity and explicitly outlines the reasoning structure, (ii) for computationally hard reasoning tasks, the more sophisticated tree structure of ToT outperforms the linear structure of CoT, and (iii) explicitly annotating important variables is important for good performance. These findings provide useful guidelines for the use of LLM in solving reasoning tasks in practice. © 2024 Association for Computational Linguistics.

关键词： Trees (mathematics)

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Enhancing Reinforcement Learning with Dense Rewards from language Model Critic

Enhancing Reinforcement Learning with Dense Rewards from Lan...

引用

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

作者： Cao, Meng Shu, Lei Yu, Lei Zhu, Yun Wichers, Nevan Liu, Yinxiao Meng, Lei School of Computer Science McGill University Canada Department of Computer Science University of Toronto Canada Mila - Québec AI Institute Canada Google Deepmind United Kingdom

ISBN: (纸本)9798891761643

Reinforcement learning (RL) can align language models with non-differentiable reward signals, such as human preferences. However, a major challenge arises from the sparsity of these reward signals - typically, there is only a single reward for an entire output. This sparsity of rewards can lead to inefficient and unstable learning. To address this challenge, our paper introduces an novel framework that utilizes the critique capability of Large language Models (LLMs) to produce intermediate-step rewards during RL training. Our approach pairs a policy model with a critic language model that provides feedback on each part of the policy's output. This feedback is then translated into token or span-level rewards that can be used to guide the RL training process. We investigate this approach under two different settings: one where the policy model is smaller and is paired with a more powerful critic model, and another where a single language model fulfills both roles. We assess our approach on three text generation tasks: sentiment control, language model detoxification, and summarization. Experimental results show that incorporating artificial intrinsic rewards significantly improve both sample efficiency and the overall performance of the policy model, supported by both automatic and human evaluation. The code is available under Google Research github. © 2024 Association for Computational Linguistics.

关键词： Reinforcement learning

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KMatrix: A Flexible Heterogeneous Knowledge Enhancement Toolkit for Large language Model

KMatrix: A Flexible Heterogeneous Knowledge Enhancement Tool...

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

作者： Wu, Shun Wu, Di Luo, Kun Zhang, XueYou Zhao, Jun Liu, Kang The Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Shanghai Artificial Intelligence Laboratory China

ISBN: (纸本)9798891761674

Knowledge-Enhanced Large language Models (K-LLMs) system enhances Large language Models (LLMs) abilities using external knowledge. Existing K-LLMs toolkits mainly focus on free-textual knowledge, lacking support for heterogeneous knowledge like tables and knowledge graphs, and fall short in comprehensive datasets, models, and user-friendly experience. To address this gap, we introduce KMatrix: a flexible heterogeneous knowledge enhancement toolkit for LLMs including verbalizing-retrieval and parsing-query methods. Our modularity and control-logic flow diagram design flexibly supports the entire lifecycle of various complex K-LLMs systems, including training, evaluation, and deployment. To assist K-LLMs system research, a series of related knowledge, datasets, and models are integrated into our toolkit, along with performance analyses of K-LLMs systems enhanced by different types of knowledge. Using our toolkit, developers can rapidly build, evaluate, and deploy their own K-LLMs systems. Our toolkit and resources are available at here. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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ADELIE: Aligning Large language Models on Information Extraction

ADELIE: Aligning Large Language Models on Information Extrac...

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

作者： Qi, Yunjia Peng, Hao Wang, Xiaozhi Xu, Bin Hou, Lei Li, Juanzi Department of Computer Science and Technology BNRist Tsinghua University China

ISBN: (纸本)9798891761643

Large language models (LLMs) usually fall short on information extraction (IE) tasks and struggle to follow the complex instructions of IE tasks. This primarily arises from LLMs not being aligned with humans, as mainstream alignment datasets typically do not include IE data. In this paper, we introduce ADELIE (Aligning large language moDELs on Information Extraction), an aligned LLM that effectively solves various IE tasks, including closed IE, open IE, and on-demand IE. We first collect and construct a high-quality alignment corpus IEInstruct for IE. Then we train ADELIESFT using instruction tuning on IEInstruct. We further train ADELIESFT with direct preference optimization (DPO) objective, resulting in ADELIEDPO. Extensive experiments on various held-out IE datasets demonstrate that our models (ADELIESFT and ADELIEDPO) achieve state-of-the-art (SoTA) performance among open-source models. We further explore the general capabilities of ADELIE, and experimental results reveal that their general capabilities do not exhibit a noticeable decline. We have released the code, data, and models to facilitate further research. © 2024 Association for Computational Linguistics.

关键词： Information retrieval

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