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

共 15353 条记录，以下是1161-1170 订阅

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Revisiting Source Context in Nearest Neighbor Machine Translation

Revisiting Source Context in Nearest Neighbor Machine Transl...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Li, Xuanhong Li, Peng Hu, Po Cent China Normal Univ Hubei Prov Key Lab Artificial Intelligence & Smar Wuhan Hubei Peoples R China Cent China Normal Univ Sch Comp Sci Wuhan Hubei Peoples R China Cent China Normal Univ Natl Language Resources Monitoring & Res Ctr Netw Wuhan Hubei Peoples R China Tsinghua Univ Inst AI Ind Res AIR Beijing Peoples R China

ISBN: (纸本)9798891760608

Nearest neighbor machine translation (kNNMT), which interpolates target token probabilities with estimates derived from additional examples, has achieved significant improvements and attracted extensive interest in recent years. However, existing research does not explicitly consider the source context when retrieving similar examples, potentially leading to suboptimal performance. To address this, we comprehensively revisit the role of source context and propose a simple and effective method for improving neural machine translation via source context enhancement, demonstrating its crucial role in both retrieving superior examples and determining more suitable interpolation coefficients. Furthermore, we reveal that the probability estimation can be further optimized by incorporating a source-aware distance calibration module. Comprehensive experiments show that our proposed approach can be seamlessly integrated with representative kNN-MT baselines, resulting in substantial improvements over these strong baselines across a number of settings and domains. Remarkably, these improvements can reach up to 1.6 BLEU points.(1)

关键词： Neural machine translation

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Reasoning with language Model is Planning with World Model

Reasoning with Language Model is Planning with World Model

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conference on empirical methods in natural language processing (EMNLP)

作者： Hao, Shibo Gu, Yi Ma, Haodi Hong, Joshua Jiahua Wang, Zhen Wang, Daisy Zhe Hu, Zhiting Univ Calif San Diego La Jolla CA 92093 USA Univ Florida Gainesville FL 32611 USA Mohamed bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9798891760608

Large language models (LLMs) have shown remarkable reasoning capabilities, particularly with chain-of-thought (CoT) prompting. However, LLMs sometimes still struggle with problems that are easy for humans, such as generating action plans to achieve given goals in an environment, or performing complex math or logical reasoning. The deficiency stems from the key fact that LLMs lack an internal world model to predict the world state (e.g., environment status, intermediate variable values) and simulate long-term outcomes of actions. This prevents LLMs from performing deliberate planning akin to human brains, which involves exploring alternative reasoning paths, anticipating future states and rewards, and iteratively refining existing reasoning steps. To overcome the limitations, we propose a new LLM reasoning framework, Reasoning via Planning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm based on Monte Carlo Tree Search for strategic exploration in the vast reasoning space. During reasoning, the LLM (as agent) incrementally builds a reasoning tree under the guidance of the LLM (as world model) and rewards, and efficiently obtains a high-reward reasoning path with a proper balance between exploration vs. exploitation. We apply RAP to various challenging reasoning problems including plan generation, math reasoning, and logical inference, and demonstrate its superiority over strong baselines. RAP with LLaMA-33B even surpasses CoT with GPT-4, achieving 33% relative improvement in a plan generation setting.(1)

关键词： Iterative methods

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What Makes a High-Quality Training Dataset for Large language Models: A Practitioners' Perspective 24

What Makes a High-Quality Training Dataset for Large Languag...

引用

39th ACM/IEEE International conference on Automated Software Engineering (ASE)

作者： Yu, Xiao Zhang, Zexian Niu, Feifei Hu, Xing Xia, Xin Grundy, John Huawei Hangzhou Peoples R China Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada Zhejiang Univ State Key Lab Blockchain & Data Secur Hangzhou Peoples R China Monash Univ Fac Informat Technol Melbourne Vic Australia Wuhan Univ Technol Chongqing Res Inst Chongqing Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Large language Models (LLMs) have demonstrated remarkable performance in various application domains, largely due to their self-supervised pre-training on extensive high-quality text datasets. However, despite the importance of constructing such datasets, many leading LLMs lack documentation of their dataset construction and training procedures, leaving LLM practitioners with a limited understanding of what makes a high-quality training dataset for LLMs. To fill this gap, we initially identified 18 characteristics of high-quality LLM training datasets, as well as 10 potential data pre-processing methods and 6 data quality assessment methods, through detailed interviews with 13 experienced LLM professionals. We then surveyed 219 LLM practitioners from 23 countries across 5 continents. We asked our survey respondents to rate the importance of these characteristics, provide a rationale for their ratings, specify the key data pre-processing and data quality assessment methods they used, and highlight the challenges encountered during these processes. From our analysis, we identified 13 crucial characteristics of high-quality LLM datasets that receive a high rating, accompanied by key rationale provided by respondents. We also identified some widely-used data pre-processing and data quality assessment methods, along with 7 challenges encountered during these processes. Based on our findings, we discuss the implications for researchers and practitioners aiming to construct high-quality training datasets for optimizing LLMs.

关键词： Large language Models High-Quality Data Practitioners' Perspective empirical Study

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Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large language Models

Tree of Clarifications: Answering Ambiguous Questions with R...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Kim, Gangwoo Kim, Sungdong Jeon, Byeongguk Park, Joonsuk Kang, Jaewoo Korea Univ Seoul South Korea NAVER Cloud Seongnam Si South Korea NAVER AI Lab Seongnam Si South Korea KAIST AI Daejeon South Korea Univ Richmond Richmond VA 23173 USA

ISBN: (纸本)9798891760608

Questions in open-domain question answering are often ambiguous, allowing multiple interpretations. One approach to handling them is to identify all possible interpretations of the ambiguous question (AQ) and to generate a long-form answer addressing them all, as suggested by Stelmakh et al. (2022). While it provides a comprehensive response without bothering the user for clarification, considering multiple dimensions of ambiguity and gathering corresponding knowledge remains a challenge. To cope with the challenge, we propose a novel framework, TREE OF CLARIFICATIONS (TOC): It recursively constructs a tree of disambiguations for the AQ-via few-shot prompting leveraging external knowledge-and uses it to generate a long-form answer. TOC outperforms existing baselines on ASQA in a few-shot setup across all metrics, while surpassing fully-supervised baselines trained on the whole training set in terms of Disambig-F1 and Disambig-ROUGE. Code is available at ***/gankim/tree-of-clarifications.

关键词： Knowledge management

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The Benefits in Shallow: Merge Decoding Across Large language Model Layers 13th

The Benefits in Shallow: Merge Decoding Across Large Languag...

引用

13th International conference on natural language processing and Chinese Computing

作者： Zhou, Yuechi Zhou, Chuyue Xie, Wenjing Wang, Xinrui Chen, Jiuchang Ni, Zhenghua Li, Juntao Soochow Univ Inst Comp Sci & Technol Suzhou Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Large language models (LLMs) have become foundational to numerous natural language processing tasks;however, decoding coherent and contextually relevant text remains a complex challenge. In openended generation, maximizing probability is often not the appropriate objective, as with sampling methods, the continuation tends to be incoherent and repetitive in various degrees. We propose Merge Decoding, merging information in the shallow layer, such as sequential information, with the final task-specific layer, thereby generating coherent and rich text. MD works across three scales of the LLaMA family(7B, 13B, 30B), achieving higher quality text in open-ended text generation (Wiki-Text, WikiNews, BookCorpus) and enhancing reasoning capabilities in downstream tasks (Gsm8k, StrategyQA) https://***/YcChou/MergeDecoding.

关键词： Large language model Decoding strategy Text generation

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CONSTRUCTURE: Benchmarking CONcept STRUCTUre REasoning for Multimodal Large language Models

CONSTRUCTURE: Benchmarking CONcept STRUCTUre REasoning for M...

引用

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

作者： Zha, Zhiwei Zhu, Xiangru Xu, Yuanyi Huang, Chenghua Liu, Jingping Li, Zhixu Wang, Xuwu Xiao, Yanghua Yang, Bei Xu, XiaoXiao Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Information Science and Engineering East China University of Science and Technology China Alibaba Group China School of Information Renmin University of China China Renmin University of China China

ISBN: (纸本)9798891761681

Multimodal Large language Models (MLLMs) have shown promising results in various tasks, but their ability to perceive the visual world with deep, hierarchical understanding similar to humans remains uncertain. To address this gap, we introduce CONSTRUCTURE, a novel concept-level benchmark to assess MLLMs' hierarchical concept understanding and reasoning abilities. Our goal is to evaluate MLLMs across four key aspects: 1) Understanding atomic concepts at different levels of abstraction;2) Performing upward abstraction reasoning across concepts;3) Achieving downward concretization reasoning across concepts;and 4) Conducting multi-hop reasoning between sibling or common ancestor concepts. Our findings indicate that even state-of-the-art multimodal models struggle with concept structure reasoning (e.g., GPT-4o averages a score of 62.1%). We summarize key findings of MLLMs in concept structure reasoning evaluation. Morever, we provide key insights from experiments using CoT prompting and fine-tuning to enhance their abilities. © 2024 Association for Computational Linguistics.

关键词： Abstracting

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LLM-driven Instruction Following: Progresses and Concerns

LLM-driven Instruction Following: Progresses and Concerns

引用

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

作者： Yin, Wenpeng Ye, Qinyuan Liu, Pengfei Ren, Xiang Schütze, Hinrich Penn State United States USC United States SJTU United States LMU Munich Germany

The progress of natural language processing (NLP) is primarily driven by machine learning that optimizes a system on a large-scale set of task-specific labeled examples. This learning paradigm limits the ability of machines to have the same capabilities as humans in handling new tasks since humans can often solve unseen tasks with a couple of examples accompanied by task instructions. In addition, we may not have a chance to prepare task-specific examples of large-volume for new tasks because we cannot foresee what task needs to be addressed next and how complex to annotate for it. Therefore, task instructions act as a novel and promising resource for supervision. This tutorial targets researchers and practitioners who are interested in AI and ML technologies for NLP generalization in a low-shot scenario. In particular, we will present a diverse thread of instruction-driven NLP studies that try to answer the following questions: (i) What is task instruction? (ii) How is the process of creating datasets and evaluating systems conducted? (iii) How to encode task instructions? (iv) When and why do some instructions work better? (v) What concerns remain in LLM-driven instruction following? We will discuss several lines of frontier research that tackle those challenges and will conclude the tutorial by outlining directions for further investigation. © 2023 Association for Computational Linguistics.

关键词： natural language processing systems

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MIBench: Evaluating Multimodal Large language Models over Multiple Images

MIBench: Evaluating Multimodal Large Language Models over Mu...

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

作者： Liu, Haowei Zhang, Xi Xu, Haiyang Shi, Yaya Jiang, Chaoya Yan, Ming Zhang, Ji Huang, Fei Yuan, Chunfeng Li, Bing Hu, Weiming MAIS Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Alibaba Group China University of Science and Technology of China China Peking University China School of Information Science and Technology ShanghaiTech University China

ISBN: (纸本)9798891761643

Built on the power of LLMs, numerous multimodal large language models (MLLMs) have recently achieved remarkable performance on various vision-language tasks. However, most existing MLLMs and benchmarks primarily focus on single-image input scenarios, leaving the performance of MLLMs when handling realistic multiple images underexplored. Although a few benchmarks consider multiple images, their evaluation dimensions and samples are very limited. In this paper, we propose a new benchmark MIBench, to comprehensively evaluate fine-grained abilities of MLLMs in multi-image scenarios. Specifically, MIBench categorizes the multi-image abilities into three scenarios: multi-image instruction (MII), multimodal knowledge-seeking (MKS) and multimodal in-context learning (MIC), and constructs 13 tasks with a total of 13K annotated samples. During data construction, for MII and MKS, we extract correct options from manual annotations and create challenging distractors to obtain multiple-choice questions. For MIC, to enable an in-depth evaluation, we set four sub-tasks and transform the original datasets into in-context learning formats. We evaluate several open-source and closed-source MLLMs on the proposed MIBench. The results reveal that although current models excel in single-image tasks, they exhibit significant shortcomings when faced with multi-image inputs, such as limited fine-grained perception, multi-image reasoning and in-context learning abilities. The annotated data of MIBench is available at https://***/datasets/StarBottle/MIBench. © 2024 Association for Computational Linguistics.

关键词： Visual languages

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RALLE: A Framework for Developing and Evaluating Retrieval-Augmented Large language Models

RALLE: A Framework for Developing and Evaluating Retrieval-A...

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

作者： Hoshi, Yasuto Miyashita, Daisuke Ng, Youyang Tatsuno, Kento Morioka, Yasuhiro Torii, Osamu Deguchi, Jun Kioxia Corporation Japan

Retrieval-augmented large language models (R-LLMs) combine pre-trained large language models (LLMs) with information retrieval systems to improve the accuracy of factual question-answering. However, current libraries for building R-LLMs provide high-level abstractions without sufficient transparency for evaluating and optimizing prompts within specific inference processes such as retrieval and generation. To address this gap, we present RALLE, an open-source framework designed to facilitate the development, evaluation, and optimization of R-LLMs for knowledge-intensive tasks. With RALLE, developers can easily develop and evaluate R-LLMs, improving hand-crafted prompts, assessing individual inference processes, and objectively measuring overall system performance quantitatively. By leveraging these features, developers can enhance the performance and accuracy of their R-LLMs in knowledge-intensive generation tasks. We open-source our code at https://***/yhoshi3/RaLLe. © 2023 Association for Computational Linguistics.

关键词： Information retrieval systems

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Eliciting Instruction-tuned Code language Models' Capabilities to Utilize Auxiliary Function for Code Generation

Eliciting Instruction-tuned Code Language Models' Capabiliti...

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

作者： Lee, Seonghyeon Kim, Suyeon Jang, Joonwon Chon, Heejae Lee, Dongha Yu, Hwanjo Department of Computer Science and Engineering POSTECH Pohang Korea Republic of Department of Artificial Intelligence POSTECH Pohang Korea Republic of Department of Artificial Intelligence Yonsei University Seoul Korea Republic of

ISBN: (纸本)9798891761681

We study the code generation behavior of instruction-tuned models built on top of code pre-trained language models when they could access an auxiliary function to implement a function. We design several ways to provide auxiliary functions to the models by adding them to the query or providing a response prefix to incorporate the ability to utilize auxiliary functions with the instruction-following capability. Our experimental results show the effectiveness of combining the base models' auxiliary function utilization ability with the instruction following ability. In particular, the performance of adopting our approaches with the open-sourced language models surpasses that of the recent powerful proprietary language models, i.e., gpt-4o. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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