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

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Learning to Write Rationally: How Information Is Distributed in Non-Native Speakers' Essays

Learning to Write Rationally: How Information Is Distributed...

引用

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

作者： Tang, Zixin van Hell, Janet G. College of Information Sciences and Technology The Pennsylvania State University United States Department of Psychology United States Center for Language Science The Pennsylvania State University United States

ISBN: (纸本)9798891761643

People tend to distribute information evenly during language production, such as when writing an essay, to improve clarity and communication. However, this may pose challenges to non-native speakers. In this study, we compared essays written by second language (L2) learners with various native language (L1) backgrounds to investigate how they distribute information in their non-native L2 written essays. We used information-based metrics, i.e., word surprisal, word entropy, and uniform information density, to estimate how writers distribute information throughout the essay to deliver information. The surprisal and constancy of entropy metrics showed that as writers' L2 proficiency increases, their essays show more native-like patterns will be in the essay, indicating more native-like mechanisms in delivering informative but less surprising *** contrast, the uniformity of information density metric showed fewer differences across L2 speakers, regardless of their L1 background and L2 proficiency, suggesting that distributing information evenly is a more universal mechanism in human language production mechanisms. This work provides a computational approach to investigate language diversity, variation, and L2 acquisition via human language production. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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QUIK: Towards End-to-end 4-Bit Inference on Generative Large language Models

QUIK: Towards End-to-end 4-Bit Inference on Generative Large...

引用

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

作者： Ashkboos, Saleh Markov, Ilia Frantar, Elias Zhong, Tingxuan Wang, Xingchen Ren, Jie Hoefler, Torsten Alistarh, Dan ETH Zurich Switzerland Institute of Science and Technology Austria Xidian University China KAUST Saudi Arabia Neural Magic Inc. United States

ISBN: (纸本)9798891761643

Large language Models (LLMs) from the GPT family have become extremely popular, leading to a race towards reducing their inference costs to allow for efficient local computation. However, the vast majority of existing work focuses on weight-only quantization, which can reduce runtime costs in the memory-bound one-token-at-a-time generative setting, but does not address costs in compute-bound scenarios, such as batched inference or prompt processing. In this paper, we address the general quantization problem, where both weights and activations should be quantized, which leads to computational improvements in general. We show that the majority of inference computations for large generative models can be performed with both weights and activations being cast to 4 bits, while at the same time maintaining good accuracy. We achieve this via a hybrid quantization strategy called QUIK that compresses most of the weights and activations to 4-bit, while keeping a small fraction of "outlier" weights and activations in higher-precision. QUIK is that it is designed with computational efficiency in mind: we provide GPU kernels matching the QUIK format with highly-efficient layer-wise runtimes, which lead to practical end-to-end throughput improvements of up to 3.4x relative to FP16 execution. We provide detailed studies for models from the OPT, LLaMA-2 and Falcon families, as well as a first instance of accurate inference using quantization plus 2:4 sparsity. Anonymized code is available here. © 2024 Association for Computational Linguistics.

关键词： Graphics processing unit

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ChatRetriever: Adapting Large language Models for Generalized and Robust Conversational Dense Retrieval

ChatRetriever: Adapting Large Language Models for Generalize...

引用

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

作者： Mao, Kelong Deng, Chenlong Chen, Haonan Mo, Fengran Liu, Zheng Sakai, Tetsuya Dou, Zhicheng Gaoling School of Artificial Intelligence Renmin University of China China Université de Montréal Québec Canada Beijing Academy of Artificial Intelligence China Waseda University Tokyo Japan

ISBN: (纸本)9798891761643

Conversational search requires accurate interpretation of user intent from complex multi-turn contexts. This paper presents ChatRetriever, which inherits the strong generalization capability of large language models to robustly represent complex conversational sessions for dense retrieval. To achieve this, we propose a simple and effective dual-learning approach that adapts LLM for retrieval via contrastive learning while enhancing the complex session understanding through masked instruction tuning on high-quality conversational instruction tuning data. Extensive experiments on five conversational search benchmarks demonstrate that ChatRetriever substantially outperforms existing conversational dense retrievers, achieving state-of-the-art performance on par with LLM-based rewriting approaches. Furthermore, ChatRetriever exhibits superior robustness in handling diverse conversational contexts. Our work highlights the potential of adapting LLMs for retrieval with complex inputs like conversational search sessions and proposes an effective approach to advance this research direction. © 2024 Association for Computational Linguistics.

关键词： Contrastive Learning

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Towards Tool Use Alignment of Large language Models

Towards Tool Use Alignment of Large Language Models

引用

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

作者： Chen, Zhi-Yuan Shen, Shiqi Shen, Guangyao Zhi, Gong Chen, Xu Lin, Yankai Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Tencent Inc. China

ISBN: (纸本)9798891761643

Recently, tool use with LLMs has become one of the primary research topics as it can help LLM generate truthful and helpful responses. Existing studies on tool use with LLMs primarily focus on enhancing the tool-calling ability of LLMs. In practice, like chat assistants, LLMs are also required to align with human values in the context of tool use. Specifically, LLMs should refuse to answer unsafe tool use relevant instructions and insecure tool responses to ensure their reliability and harmlessness. At the same time, LLMs should demonstrate autonomy in tool use to reduce the costs associated with tool calling. To tackle this issue, we first introduce the principle that LLMs should follow in tool use scenarios: H2A. The goal of H2A is to align LLMs with helpfulness, harmlessness, and autonomy. In addition, we propose ToolAlign, a dataset comprising instruction-tuning data and preference data to align LLMs with the H2A principle for tool use. Based on ToolAlign, we develop LLMs by supervised fine-tuning and preference learning, and experimental results demonstrate that the LLMs exhibit remarkable tool-calling capabilities, while also refusing to engage with harmful content, and displaying a high degree of autonomy in tool utilization. The code and datasets are available at: https://***/zhiyuanc2001/ToolAlign. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing Systematic Decompositional natural language Inference Using Informal Logic

Enhancing Systematic Decompositional Natural Language Infere...

引用

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

作者： Weir, Nathaniel Sanders, Kate Weller, Orion Sharma, Shreya Jiang, Dongwei Jiang, Zhengping Mishra, Bhavana Dalvi Tafjord, Oyvind Jansen, Peter Clark, Peter Van Durme, Benjamin Johns Hopkins University United States Allen Institute for AI United States University of Arizona United States

ISBN: (纸本)9798891761643

Recent language models enable new opportunities for structured reasoning with text, such as the construction of intuitive, proof-like textual entailment trees without relying on brittle formal logic (Tafjord et al., 2022;Weir et al., 2024). However, progress in this direction has been hampered by a long-standing lack of a clear protocol for determining what valid compositional entailment is. This absence causes noisy datasets and limited performance gains by modern neuro-symbolic engines. To address these problems, we formulate a consistent and theoretically grounded approach to annotating decompositional entailment and evaluate its impact on LLM-based textual inference. We find that our new dataset, RDTE (Recognizing Decompositional Textual Entailment), has a substantially higher internal consistency (+9%) than prior decompositional entailment datasets. We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in an entailment tree reasoning engine significantly improves both accuracy and proof quality, illustrating the practical benefit of this advance for textual inference. © 2024 Association for Computational Linguistics.

关键词： Trees (mathematics)

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A Systematic Survey and Critical Review on Evaluating Large language Models: Challenges, Limitations, and Recommendations

A Systematic Survey and Critical Review on Evaluating Large ...

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

作者： Laskar, Md Tahmid Rahman Alqahtani, Sawsan Bari, M. Saiful Rahman, Mizanur Khan, Mohammad Abdullah Matin Khan, Haidar Jahan, Israt Bhuiyan, Md Amran Hossen Tan, Chee Wei Parvez, Md Rizwan Hoque, Enamul Joty, Shafiq Huang, Jimmy Xiangji York University Canada Princess Nourah Bint Abdulrahman University Saudi Arabia Nanyang Technological University Singapore National Center for AI Saudi Arabia Qatar Dialpad Canada Inc. Canada Royal Bank of Canada Canada Salesforce Research Singapore

ISBN: (纸本)9798891761643

Large language Models (LLMs) have recently gained significant attention due to their remarkable capabilities in performing diverse tasks across various domains. However, a thorough evaluation of these models is crucial before deploying them in real-world applications to ensure they produce reliable performance. Despite the well-established importance of evaluating LLMs in the community, the complexity of the evaluation process has led to varied evaluation setups, causing inconsistencies in findings and interpretations. To address this, we systematically review the primary challenges and limitations causing these inconsistencies and unreliable evaluations in various steps of LLM evaluation. Based on our critical review, we present our perspectives and recommendations to ensure LLM evaluations are reproducible, reliable, and robust. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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MMCode: Benchmarking Multimodal Large language Models in Code Generation with Visually Rich Programming Problems

MMCode: Benchmarking Multimodal Large Language Models in Cod...

引用

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

作者： Li, Kaixin Tian, Yuchen Hu, Qisheng Luo, Ziyang Huang, Zhiyong Ma, Jing National University of Singapore Singapore The University of Hong Kong Hong Kong Nanyang Technological University Singapore Hong Kong Baptist University Hong Kong

ISBN: (纸本)9798891761681

Programming often involves converting detailed and complex specifications into code, a process during which developers typically utilize visual aids to more effectively convey concepts. While recent developments in Large Multimodal Models have demonstrated remarkable abilities in visual reasoning and mathematical tasks, there is little work on investigating whether these models can effectively interpret visual elements for code generation. To this end, we present MMCode, the first multimodal coding dataset for evaluating algorithmic problem-solving skills in visually rich contexts. MMCode contains 3,548 questions and 6,620 images collected from real-world programming challenges harvested from 10 code competition websites, presenting significant challenges due to the extreme demand for reasoning abilities. Our experiment results show that current state-of-the-art models struggle to solve these problems. The results highlight the lack of powerful vision-code models, and we hope MMCode can serve as an inspiration for future works in this domain. The data and code are publicly available. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Mitigate Extrinsic Social Bias in Pre-trained language Models via Continuous Prompts Adjustment

Mitigate Extrinsic Social Bias in Pre-trained Language Model...

引用

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

作者： Dai, Yiwei Gu, Hengrui Wang, Ying Wang, Xin School of Artificial Intelligence Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9798891761643

Although pre-trained language models (PLMs) have been widely used in natural language understandings (NLU), they are still exposed to fairness issues. Most existing extrinsic debiasing methods rely on manually curated word lists for each sensitive groups to modify training data or to add regular constraints. However, these word lists are often limited by length and scope, resulting in the degradation performance of extrinsic bias mitigation. To address the aforementioned issues, we propose a Continuous Prompts Adjustment Debiasing method (CPAD), which generates continuous token lists from the entire vocabulary space and uses them to bridge the gap between outputs and targets in fairness learning process. Specifically, CPAD encapsulates fine-tuning objective and debiasing objectives into several independent prompts. To avoid the limitation of manual word lists, in fairness learning phase, we extract outputs from the entire vocabulary space via fine-tuned PLM. Then, we aggregate the outputs from the same sensitive group as continuous token lists to map the outputs into protected attribute labels. Finally, after we learn the debiasing prompts in the perspective of adversarial learning, we improve fairness by adjusting continuous prompts at model inference time. Through extensive experiments on three NLU tasks, we evaluate the debiasing performance from the perspectives of group fairness and fairness through unawareness. The experimental results show that CPAD outperforms all baselines in term of single and two-attributes debiasing performance. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Context-aware Watermark with Semantic Balanced Green-red Lists for Large language Models

Context-aware Watermark with Semantic Balanced Green-red Lis...

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

作者： Guo, Yuxuan Tian, Zhiliang Song, Yiping Liu, Tianlun Ding, Liang Li, Dongsheng National University of Defense Technology China Zhejiang University China

ISBN: (纸本)9798891761643

Watermarking enables people to determine whether the text is generated by a specific model. It injects a unique signature based on the "green-red" list that can be tracked during detection, where the words in green lists are encouraged to be generated. Recent researchers propose to fix the green/red lists or increase the proportion of green tokens to defend against paraphrasing attacks. However, these methods cause degradation of text quality due to semantic disparities between the watermarked text and the unwatermarked text. In this paper, we propose a semantic-aware watermark method that considers contexts to generate a semantic-aware key to split a semantically balanced green/red list for watermark injection. The semantic balanced list reduces the performance drop due to adding bias on green lists. To defend against paraphrasing attacks, we generate the watermark key considering the semantics of contexts via locally sensitive hashing. To improve the text quality, we propose to split green/red lists considering semantics to enable the green list to cover almost all semantics. We also dynamically adapt the bias to balance text quality and robustness. The experiments show our advantages in both robustness and text quality comparable to existing baselines. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large language Models in Code Generation

AMR-Evol: Adaptive Modular Response Evolution Elicits Better...

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

作者： Luo, Ziyang Li, Xin Lin, Hongzhan Ma, Jing Bing, Lidong Hong Kong Baptist University Hong Kong Alibaba DAMO Academy China

ISBN: (纸本)9798891761643

The impressive performance of proprietary LLMs like GPT4 in code generation has led to a trend to replicate these capabilities in open-source models through knowledge distillation (e.g. Code Evol-Instruct). However, these efforts often neglect the crucial aspect of response quality, relying heavily on teacher models for direct response distillation. This paradigm, especially for complex instructions, can degrade the quality of synthesized data, compromising the knowledge distillation process. To this end, our study introduces the Adaptive Modular Response Evolution (AMR-Evol) framework, which employs a two-stage process to refine response distillation. The first stage, modular decomposition, breaks down the direct response into more manageable sub-modules. The second stage, adaptive response evolution, automatically evolves the response with the related function modules. Our experiments with three popular code benchmarks-HumanEval, MBPP, and EvalPlus-attests to the superiority of the AMR-Evol framework over baseline response distillation methods. By comparing with the open-source Code LLMs trained on a similar scale of data, we observed performance enhancements: more than +3.0 points on HumanEval-Plus and +1.0 points on MBPP-Plus, which underscores the effectiveness of our framework. Our codes are available at https://***/ChiYeungLaw/AMR-Evol. © 2024 Association for Computational Linguistics.

关键词： Open source software

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