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Home AI & Sentiment Analysis

Benchmarking LLM Proficiency in Scientific Literature Analysis

AiNEWS2025 by AiNEWS2025
2024-12-10
in AI & Sentiment Analysis
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Benchmarking LLM Proficiency in Scientific Literature Analysis
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[Submitted on 4 Mar 2024 (v1), last revised 18 Oct 2024 (this version, v5)]
Authors:Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Changxin Wang, Zhifeng Gao, Hongshuai Wang, Yongge Li, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke

View a PDF of the paper titled SciAssess: Benchmarking LLM Proficiency in Scientific Literature Evaluation, by Hengxing Cai and 22 different authors

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Summary:Latest breakthroughs in Giant Language Fashions (LLMs) have revolutionized scientific literature evaluation. Nevertheless, current benchmarks fail to adequately consider the proficiency of LLMs on this area, significantly in eventualities requiring higher-level skills past mere memorization and the dealing with of multimodal information. In response to this hole, we introduce SciAssess, a benchmark particularly designed for the excellent analysis of LLMs in scientific literature evaluation. It goals to totally assess the efficacy of LLMs by evaluating their capabilities in Memorization (L1), Comprehension (L2), and Evaluation & Reasoning (L3). It encompasses quite a lot of duties drawn from numerous scientific fields, together with biology, chemistry, materials, and drugs. To make sure the reliability of SciAssess, rigorous high quality management measures have been applied, making certain accuracy, anonymization, and compliance with copyright requirements. SciAssess evaluates 11 LLMs, highlighting their strengths and areas for enchancment. We hope this analysis helps the continuing growth of LLM functions in scientific literature evaluation. SciAssess and its assets can be found at url{this https URL}.

Submission historical past

From: Hengxing Cai [view email]
[v1]
Mon, 4 Mar 2024 12:19:28 UTC (4,202 KB)
[v2]
Fri, 15 Mar 2024 13:27:31 UTC (8,174 KB)
[v3]
Sat, 15 Jun 2024 15:45:47 UTC (7,855 KB)
[v4]
Tue, 18 Jun 2024 05:45:33 UTC (7,855 KB)
[v5]
Fri, 18 Oct 2024 06:52:17 UTC (5,754 KB)

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#Benchmarking #LLM #Proficiency #Scientific #Literature #Evaluation


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