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检索条件"任意字段=Conference on empirical methods in natural language processing"
15205 条 记 录,以下是211-220 订阅
排序:
language Concept Erasure for language-invariant Dense Retrieval
Language Concept Erasure for Language-invariant Dense Retrie...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Huang, Zhiqi Yu, Puxuan Ravfogel, Shauli Allan, James Capital One United States Snowflake Inc. United States Bar-Ilan University Israel University of Massachusetts Amherst United States
Multilingual models aim for language-invariant representations but still prominently encode language identity. This, along with the scarcity of high-quality parallel retrieval data, limits their performance in retriev... 详细信息
来源: 评论
GeoLM: Empowering language Models for Geospatially Grounded language Understanding
GeoLM: Empowering Language Models for Geospatially Grounded ...
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conference on empirical methods in natural language processing (EMNLP)
作者: Li, Zekun Zhou, Wenxuan Chiang, Yao-Yi Chen, Muhao Univ Minnesota Twin Cities Dept Comp Sci & Engn Minneapolis MN 55455 USA Univ Southern Calif Dept Comp Sci Los Angeles CA USA Univ Calif Davis Dept Comp Sci Davis CA USA
Humans subconsciously engage in geospatial reasoning when reading articles. We recognize place names and their spatial relations in text and mentally associate them with their physical locations on Earth. Although pre... 详细信息
来源: 评论
Revisiting Automated Topic Model Evaluation with Large language Models
Revisiting Automated Topic Model Evaluation with Large Langu...
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conference on empirical methods in natural language processing (EMNLP)
作者: Stammbach, Dominik Zouhar, Vilem Hoyle, Alexander Sachan, Mrinmaya Ash, Elliott Swiss Fed Inst Technol Zurich Switzerland Univ Maryland College Pk MD 20742 USA
Topic models help make sense of large text collections. Automatically evaluating their output and determining the optimal number of topics are both longstanding challenges, with no effective automated solutions to dat... 详细信息
来源: 评论
Predicate Debiasing in Vision-language Models Integration for Scene Graph Generation Enhancement
Predicate Debiasing in Vision-Language Models Integration fo...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Wang, Yuxuan Liu, Xiaoyuan Nanyang Technological University Singapore
Scene Graph Generation (SGG) provides basic language representation of visual scenes, requiring models to grasp complex and diverse semantics between objects. This complexity and diversity in SGG leads to underreprese... 详细信息
来源: 评论
To Ask LLMs about English Grammaticality, Prompt Them in a Different language
To Ask LLMs about English Grammaticality, Prompt Them in a D...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Behzad, Shabnam Zeldes, Amir Schneider, Nathan Georgetown University United States
In addition to asking questions about facts in the world, some internet users-in particular, second language learners-ask questions about language itself. Depending on their proficiency level and audience, they may po... 详细信息
来源: 评论
Creative natural language Generation
Creative Natural Language Generation
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2023 conference on empirical methods in natural language processing, EMNLP 2023
作者: Chakrabarty, Tuhin Padmakumar, Vishakh He, He Peng, Nanyun Columbia University United States New York University United States University of California Los Angeles United States
来源: 评论
Synthetic Data Generation with Large language Models for Text Classification: Potential and Limitations
Synthetic Data Generation with Large Language Models for Tex...
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conference on empirical methods in natural language processing (EMNLP)
作者: Li, Zhuoyan Zhu, Hangxiao Lu, Zhuoran Yin, Ming Purdue Univ W Lafayette IN 47907 USA Washington Univ St Louis MO 63110 USA
The collection and curation of high-quality training data is crucial for developing text classification models with superior performance, but it is often associated with significant costs and time investment. Research... 详细信息
来源: 评论
Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large language Models
Semi-automatic Data Enhancement for Document-Level Relation ...
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conference on empirical methods in natural language processing (EMNLP)
作者: Li, Junpeng Jia, Zixia Zheng, Zilong BIGAI Natl Key Lab Gen Artificial Intelligence Beijing Peoples R China
Document-level Relation Extraction (DocRE), which aims to extract relations from a long context, is a critical challenge in achieving fine-grained structural comprehension and generating interpretable document represe... 详细信息
来源: 评论
FROG: Evaluating Fuzzy Reasoning of Generalized Quantifiers in Large language Models
FROG: Evaluating Fuzzy Reasoning of Generalized Quantifiers ...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Li, Yiyuan Sun, Shichao Liu, Pengfei Shanghai Jiao Tong University China UNC-Chapel Hill United States The Hong Kong Polytechnic University Hong Kong
Fuzzy reasoning is vital due to the frequent use of imprecise information in daily contexts. However, the ability of current large language models (LLMs) to handle such reasoning remains largely uncharted. In this pap... 详细信息
来源: 评论
Consolidating Ranking and Relevance Predictions of Large language Models through Post-processing
Consolidating Ranking and Relevance Predictions of Large Lan...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Yan, Le Qin, Zhen Zhuang, Honglei Jagerman, Rolf Wang, Xuanhui Bendersky, Michael Oosterhuis, Harrie Google Research Mountain ViewCA94043 United States
The powerful generative abilities of large language models (LLMs) show potential in generating relevance labels for search applications. Previous work has found that directly asking about relevancy, such as "How ... 详细信息
来源: 评论