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检索条件"任意字段=Proceedings of the Conference on Empirical Methods in Natural Language Processing"
7707 条 记 录,以下是41-50 订阅
排序:
RAR: Retrieval-augmented retrieval for code generation in low-resource languages
RAR: Retrieval-augmented retrieval for code generation in lo...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Dutta, Avik Singh, Mukul Gulwani, Sumit Le, Vu Verbruggen, Gust Microsoft Bangalore India Microsoft Redmond United States Microsoft Keerbergen Belgium
language models struggle in generating code for low-resource programming languages, since these are underrepresented in training data. Either examples or documentation are commonly used for improved code generation. W... 详细信息
来源: 评论
Computational Meme Understanding: A Survey
Computational Meme Understanding: A Survey
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Nguyen, Khoi P.N. Ng, Vincent Human Language Technology Research Institute University of Texas Dallas United States
Computational Meme Understanding, which concerns the automated comprehension of memes, has garnered interest over the last four years and is facing both substantial opportunities and challenges. We survey this emergin... 详细信息
来源: 评论
language, OCR, Form Independent (LOFI) pipeline for Industrial Document Information Extraction
Language, OCR, Form Independent (LOFI) pipeline for Industri...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Yoon, Chang Oh Lee, Wonbeen Jang, Seokhwan Choi, Kyuwon Jung, Minsung Choi, Daewoo AgileSoDA Korea Republic of Hankuk University of Foreign Studies Korea Republic of
This paper presents LOFI (language, OCR, Form Independent), a pipeline for Document Information Extraction (DIE) in Low-Resource language (LRL) business documents. LOFI pipeline solves language, Optical Character Reco... 详细信息
来源: 评论
Automated Essay Scoring: A Reflection on the State of the Art
Automated Essay Scoring: A Reflection on the State of the Ar...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Li, Shengjie Ng, Vincent Human Language Technology Research Institute University of Texas Dallas United States
While steady progress has been made on the task of automated essay scoring (AES) in the past decade, much of the recent work in this area has focused on developing models that beat existing models on a standard evalua... 详细信息
来源: 评论
Arcee’s MergeKit: A Toolkit for Merging Large language Models
Arcee’s MergeKit: A Toolkit for Merging Large Language Mode...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Goddard, Charles Siriwardhana, Shamane Ehghaghi, Malikeh Meyers, Luke Karpukhin, Vlad Benedict, Brian McQuade, Mark Solawetz, Jacob Arcee FL United States
The rapid growth of open-source language models provides the opportunity to merge model checkpoints, combining their parameters to improve performance and versatility. Advances in transfer learning have led to numerou... 详细信息
来源: 评论
Fill In The Gaps: Model Calibration and Generalization with Synthetic Data
Fill In The Gaps: Model Calibration and Generalization with ...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Ba, Yang Mancenido, Michelle V. Pan, Rong School of Computing and Augmented Intelligence Arizona State University United States School of Mathematical and Natural Sciences Arizona State University United States
As machine learning models continue to swiftly advance, calibrating their performance has become a major concern prior to practical and widespread implementation. Most existing calibration methods often negatively imp... 详细信息
来源: 评论
Self-AMPLIFY: Improving Small language Models with Self Post Hoc Explanations
Self-AMPLIFY: Improving Small Language Models with Self Post...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Bhan, Milan Vittaut, Jean-Noël Chesneau, Nicolas Lesot, Marie-Jeanne Sorbonne Université CNRS LIP6 ParisF-75005 France Ekimetrics Paris France
Incorporating natural language rationales in the prompt and In-Context Learning (ICL) have led to a significant improvement of Large language Models (LLMs) performance. However, generating high-quality rationales requ... 详细信息
来源: 评论
An empirical Study of Multilingual Reasoning Distillation for Question Answering
An Empirical Study of Multilingual Reasoning Distillation fo...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Payoungkhamdee, Patomporn Limkonchotiwat, Peerat Baek, Jinheon Manakul, Potsawee Udomcharoenchaikit, Can Chuangsuwanich, Ekapol Nutanong, Sarana School of Information Science and Technology VISTEC Thailand AI Singapore Singapore KAIST Korea Republic of SCB 10X Thailand Department of Computer Engineering Chulalongkorn University Thailand
Reasoning is one crucial capability in Large language Models (LLMs), allowing them to perform complex tasks such as solving math problems and multi-step planning. While reasoning capability can emerge in larger models... 详细信息
来源: 评论
language Agents: Foundations, Prospects, and Risks
Language Agents: Foundations, Prospects, and Risks
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Su, Yu Yang, Diyi Yao, Shunyu Yu, Tao The Ohio State University United States Stanford University United States Princeton University United States University of Hong Kong Hong Kong
来源: 评论
SimLLM: Detecting Sentences Generated by Large language Models Using Similarity between the Generation and its Re-generation
SimLLM: Detecting Sentences Generated by Large Language Mode...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Nguyen-Son, Hoang-Quoc Dao, Minh-Son Zettsu, Koji National Institute of Information and Communications Technology Japan
Large language models have emerged as a significant phenomenon due to their ability to produce natural text across various applications. However, the proliferation of generated text raises concerns regarding its poten... 详细信息
来源: 评论