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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981-990 订阅

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TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities

TransferTOD: A Generalizable Chinese Multi-Domain Task-Orien...

引用

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

作者： Zhang, Ming Huang, Caishuang Wu, Yilong Liu, Shichun Zheng, Huiyuan Dong, Yurui Shen, Yujiong Dou, Shihan Zhao, Jun Ye, Junjie Zhang, Qi Gui, Tao Huang, Xuanjing School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Institute of Modern Languages and Linguistics Fudan University China

ISBN: (纸本)9798891761643

Task-oriented dialogue (TOD) systems aim to efficiently handle task-oriented conversations, including information collection. How to utilize TOD accurately, efficiently and effectively for information collection has always been a critical and challenging task. Recent studies have demonstrated that Large language Models (LLMs) excel in dialogue, instruction generation, and reasoning, and can significantly enhance the performance of TOD through fine-tuning. However, current datasets primarily cater to user-led systems and are limited to predefined specific scenarios and slots, thereby necessitating improvements in the proactiveness, diversity, and capabilities of TOD. In this study, we present a detailed multi-domain task-oriented data construction process for conversations, and a Chinese dialogue dataset generated based on this process, TransferTOD, which authentically simulates human-computer dialogues in 30 popular life service scenarios. Leveraging this dataset, we trained a model using full-parameter fine-tuning called TransferTOD-7B, showcasing notable abilities in slot filling and questioning. Our work has demonstrated its strong generalization capabilities in various downstream scenarios, significantly enhancing both data utilization efficiency and system performance. The data is released in https://***/KongLongGeFDU/TransferTOD. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

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IAEval: A Comprehensive Evaluation of Instance Attribution on natural language Understanding

IAEval: A Comprehensive Evaluation of Instance Attribution o...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Gni, Peijian Shen, Yaozong Wang, Lijie Wang, Quan Wu, Hua Mao, Zhendong Univ Sci & Technol China Hefei Peoples R China Baidu Inc Beijing Peoples R China Beijing Univ Posts & Telecommun MOE Key Lab Trustworthy Distributed Comp & Serv Beijing Peoples R China

ISBN: (纸本)9798891760615

Instance attribution (IA) aims to identify the training instances leading to the prediction of a test example, helping researchers understand the dataset better and optimize data processing. While many IA methods have been proposed recently, how to evaluate them still remains open. Previous evaluations of IA only focus on one or two dimensions and are not comprehensive. In this work, we introduce IAEval for IA methods, a systematic and comprehensive evaluation scheme covering four significant requirements: sufficiency, completeness, stability and plausibility. We elaborately design novel metrics to measure these requirements for the first time. Three representative IA methods are evaluated under IAEval on four natural language understanding datasets. Extensive experiments confirmed the effectiveness of IAEval and exhibited its ability to provide comprehensive comparison among IA methods. With IAEval, researchers can choose the most suitable IA methods for applications like model debugging.

关键词： Data handling

来源：评论

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DecoMT: Decomposed Prompting for Machine Translation Between Related languages using Large language Models

DecoMT: Decomposed Prompting for Machine Translation Between...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Puduppully, Ratish Kunchukuttan, Anoop Dabre, Raj Aw, Ai Ti Chen, Nancy F. ASTAR Inst Infocomm Res I2R Singapore Singapore CNRS CREATE Singapore Singapore Microsoft Bangalore Karnataka India Natl Inst Informat & Communicat Technol Tokyo Japan IIT Madras Madras Tamil Nadu India AI4Bharat Madras Tamil Nadu India

ISBN: (纸本)9798891760608

This study investigates machine translation between related languages i.e., languages within the same family that share linguistic characteristics such as word order and lexical similarity. Machine translation through few-shot prompting leverages a small set of translation pair examples to generate translations for test sentences. This procedure requires the model to learn how to generate translations while simultaneously ensuring that token ordering is maintained to produce a fluent and accurate translation. We propose that for related languages, the task of machine translation can be simplified by leveraging the monotonic alignment characteristic of such languages. We introduce DecoMT, a novel approach of few-shot prompting that decomposes the translation process into a sequence of word chunk translations. Through automatic and human evaluation conducted on multiple related language pairs across various language families, we demonstrate that our proposed approach of decomposed prompting surpasses multiple established few-shot baseline approaches. For example, DecoMT outperforms the strong fewshot prompting BLOOM model with an average improvement of 8 chrF++ scores across the examined languages.

关键词： Machine translation

来源：评论

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Sparse Low-rank Adaptation of Pre-trained language Models

Sparse Low-rank Adaptation of Pre-trained Language Models

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Ding, Ning Lv, Xingtai Wang, Qiaosen Chen, Yulin Zhou, Bowen Liu, Zhiyuan Sun, Maosong Tsinghua Univ Dept Elect Engn Beijing Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ BNRIST IAI Beijing Peoples R China Univ Chicago Dept Stat Chicago IL USA

ISBN: (纸本)9798891760608

Fine-tuning pre-trained large language models in a parameter-efficient manner is widely studied for its effectiveness and efficiency. The popular method of low-rank adaptation (LoRA) offers a notable approach, hypothesizing that the adaptation process is intrinsically low-dimensional. Although LoRA has demonstrated commendable performance, it is implemented with a fixed and unalterable intrinsic rank that might not always be the ideal choice. Recognizing the need for more flexible adaptation, we extend the methodology of LoRA to an innovative approach we call sparse low-rank adaptation (SoRA) that enables dynamic adjustments to the intrinsic rank during the adaptation process. We achieve this through the incorporation of a gate unit optimized with proximal gradient method in the training stage, controlling the cardinality of rank under the sparsity of the gate. In the subsequent inference stage, we eliminate the parameter blocks corresponding to the zeroed-out ranks, to reduce each SoRA module back to a concise yet rank-optimal LoRA. Our approach strengthens the representation power of LoRA by initializing it with a higher rank, while efficiently taming a temporarily increased number of parameters via updating in a sparse way. We further introduce a sparsifying scheduler for SoRA, aiming to examine the impact of the number of non-zero parameters on the model's memorization and generalization. Our experimental results demonstrate that SoRA can outperform other baselines even with 70% retained parameters and 70% training time.

关键词： Gradient methods

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PaperMage: A Unified Toolkit for processing, Representing, and Manipulating Visually-Rich Scientific Documents

PaperMage: A Unified Toolkit for Processing, Representing, a...

引用

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

作者： Lo, Kyle Shen, Zejiang Newman, Benjamin Chang, Joseph Chee Authur, Russell Bransom, Erin Candra, Stefan Chandrasekhar, Yoganand Huff, Regan Kuehl, Bailey Singh, Amanpreet Wilhelm, Chris Zamarron, Angele Hearst, Marti A. Weld, Daniel S. Downey, Doug Soldaini, Luca Allen Institute for AI United States Massachusetts Institute of Technology United States University of California Berkeley United States University of Washington United States Northwestern University United States

Despite growing interest in applying natural language processing (NLP) and computer vision (CV) models to the scholarly domain, scientific documents remain challenging to work with. They’re often in difficult-to-use PDF formats, and the ecosystem of models to process them is fragmented and incomplete. We introduce papermage, an open-source Python toolkit for analyzing and processing visually-rich, structured scientific documents. papermage offers clean and intuitive abstractions for seamlessly representing and manipulating both textual and visual document elements. papermage achieves this by integrating disparate state-of-the-art NLP and CV models into a unified framework, and provides turnkey recipes for common scientific document processing use-cases. papermage has powered multiple research prototypes of AI applications over scientific documents, along with Semantic Scholar’s large-scale production system for processing millions of PDFs. © 2023 Association for Computational Linguistics.

关键词： natural language processing systems

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MVP-Bench: Can Large Vision-language Models Conduct Multi-level Visual Perception Like Humans?

MVP-Bench: Can Large Vision-Language Models Conduct Multi-le...

引用

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

作者： Li, Guanzhen Xie, Yuxi Kan, Min-Yen National University of Singapore Singapore

ISBN: (纸本)9798891761681

Humans perform visual perception at multiple levels, including low-level object recognition and high-level semantic interpretation such as behavior understanding. Subtle differences in low-level details can lead to substantial changes in high-level perception. For example, substituting the shopping bag held by a person with a gun suggests violent behavior, implying criminal or violent activity. Despite significant advancements in various multimodal tasks, Large Visual language Models (LVLMs) remain unexplored in their capabilities to conduct such multi-level visual perceptions. To investigate the perception gap between LVLMs and humans, we introduce MVP-Bench, the first visual-language benchmark systematically evaluating both low- and high-level visual perception of LVLMs. We construct MVP-Bench across natural and synthetic images to investigate how manipulated content influences model perception. Using MVP-Bench, we diagnose the visual perception of 10 open-source and 2 closed-source LVLMs, showing that high-level perception tasks significantly challenge existing LVLMs. The state-of-the-art GPT-4o only achieves an accuracy of 56% on Yes/No questions, compared with 74% in low-level scenarios. Furthermore, the performance gap between natural and manipulated images indicates that current LVLMs do not generalize in understanding the visual semantics of synthetic images as humans do. Our data and code are publicly available at https://***/GuanzhenLi/MVP-Bench. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Self-Influence Guided Data Reweighting for language Model Pre-training

Self-Influence Guided Data Reweighting for Language Model Pr...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Thakkar, Megh Bolukbasi, Tolga Ganapathy, Sriram Vashishth, Shikhar Chandar, Sarath Talukdar, Partha Mila Quebec AI Inst Montreal PQ Canada Google Deepmind London England Google Res India Bangalore Karnataka India Indian Inst Sci Bangalore Karnataka India Polytech Montreal Montreal PQ Canada Canada CIFAR AI Chair Montreal PQ Canada

ISBN: (纸本)9798891760608

language Models (LMs) pre-trained with self-supervision on large text corpora have become the default starting point for developing models for various NLP tasks. Once the pre-training corpus has been assembled, all data samples in the corpus are treated with equal importance during LM pre-training. However, due to varying levels of relevance and quality of data, equal importance to all the data samples may not be the optimal choice. While data reweighting has been explored in the context of task-specific supervised learning and LM fine-tuning, model-driven reweighting for pre-training data has not been explored. We fill this important gap and propose PRESENCE, a method for jointly reweighting samples by leveraging self-influence (SI) scores as an indicator of sample importance and pre-training. PRESENCE promotes novelty and stability for model pre-training. Through extensive analysis spanning multiple model sizes, datasets, and tasks, we present PRESENCE as an important first step in the research direction of sample reweighting for pre-training language models.

关键词： Computational linguistics

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Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs

Unveiling and Consulting Core Experts in Retrieval-Augmented...

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

作者： Zhou, Xin Nie, Ping Guo, Yiwen Wei, Haojie Zhang, Zhanqiu Minervini, Pasquale Ma, Ruotian Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University Shanghai China LightSpeed Studios Tencent China Institute of Modern Languages and Linguistics Fudan University Shanghai China Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Informatics and ELLIS University of Edinburgh United Kingdom

ISBN: (纸本)9798891761643

Retrieval-Augmented Generation (RAG) significantly improved the ability of Large language Models (LLMs) to solve knowledge-intensive tasks. While existing research seeks to enhance RAG performance by retrieving higher-quality documents or designing RAG-specific LLMs, the internal mechanisms within LLMs that contribute to the effectiveness of RAG systems remain underexplored. In this paper, we aim to investigate these internal mechanisms within the popular Mixture-of-Expert (MoE)based LLMs and demonstrate how to improve RAG by examining expert activations in these LLMs. Our controlled experiments reveal that several core groups of experts are primarily responsible for RAG-related behaviors. The activation of these core experts can signify the model's inclination towards external/internal knowledge and adjust its behavior. For instance, we identify core experts that can (1) indicate the sufficiency of the model's internal knowledge, (2) assess the quality of retrieved documents, and (3) enhance the model's ability to utilize context. Based on these findings, we propose several strategies to enhance RAG's efficiency and effectiveness through expert activation. Experimental results across various datasets and MoE-based LLMs show the effectiveness of our method. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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PromptKD: Distilling Student-Friendly Knowledge for Generative language Models via Prompt Tuning

PromptKD: Distilling Student-Friendly Knowledge for Generati...

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

作者： Kim, Gyeongman Jang, Doohyuk Yang, Eunho Korea Republic of AITRICS Korea Republic of

ISBN: (纸本)9798891761681

Recent advancements in large language models (LLMs) have raised concerns about inference costs, increasing the need for research into model compression. While knowledge distillation (KD) is a prominent method for this, research on KD for generative language models like LLMs is relatively sparse, and the approach of distilling student-friendly knowledge, which has shown promising performance in KD for classification models, remains unexplored in generative language models. To explore this approach, we propose PromptKD, a simple yet effective method that utilizes prompt tuning - for the first time in KD - to enable generative language models to transfer student-friendly knowledge. Unlike previous works in classification that require fine-tuning the entire teacher model for extracting student-friendly knowledge, PromptKD achieves similar effects by adding a small number of prompt tokens and tuning only the prompt with student guidance. Extensive experiments on instruction-following datasets show that PromptKD achieves state-of-the-art performance while adding only 0.0007% of the teacher's parameters as prompts. Further analysis suggests that distilling student-friendly knowledge alleviates exposure bias effectively throughout the entire training process, leading to performance enhancements. © 2024 Association for Computational Linguistics.

关键词： Generative adversarial networks

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Learning to Use Tools via Cooperative and Interactive Agents with Large language Models

Learning to Use Tools via Cooperative and Interactive Agents...

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

作者： Shi, Zhengliang Gao, Shen Chen, Xiuyi Feng, Yue Yan, Lingyong Shi, Haibo Yin, Dawei Ren, Pengjie Verberne, Suzan Ren, Zhaochun Shandong University China University of Electronic Science and Technology of China China Baidu Inc. Beijing China University of Birmingham Birmingham United Kingdom Leiden University Leiden Netherlands

ISBN: (纸本)9798891761681

Tool learning empowers large language models (LLMs) as agents to use external tools and extend their utility. Existing methods employ one single LLM-based agent to iteratively select and execute tools, thereafter incorporating execution results into the next action prediction. Despite their progress, these methods suffer from performance degradation when addressing practical tasks due to: (1) the pre-defined pipeline with restricted flexibility to calibrate incorrect actions, and (2) the struggle to adapt a general LLM-based agent to perform a variety of specialized actions. To mitigate these problems, we propose CONAGENTS, a Cooperative and interactive Agents framework, which coordinates three specialized agents for tool selection, tool execution, and action calibration separately. CONAGENTS introduces two communication protocols to enable the flexible cooperation of agents. To effectively generalize the CONAGENTS into open-source models, we also propose specialized action distillation, enhancing their ability to perform specialized actions in our framework. Our extensive experiments on three datasets show that the LLMs, when equipped with the CONAGENTS, outperform baselines with substantial improvement (i.e., up to 14% higher success rate). © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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