SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866909353704947712 |
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| author | Cai, Hengxing Cai, Xiaochen Chang, Junhan Li, Sihang Yao, Lin Wang, Changxin Gao, Zhifeng Wang, Hongshuai Li, Yongge Lin, Mujie Yang, Shuwen Wang, Jiankun Xu, Mingjun Huang, Jin Fang, Xi Zhuang, Jiaxi Yin, Yuqi Li, Yaqi Chen, Changhong Cheng, Zheng Zhao, Zifeng Zhang, Linfeng Ke, Guolin |
| author_facet | Cai, Hengxing Cai, Xiaochen Chang, Junhan Li, Sihang Yao, Lin Wang, Changxin Gao, Zhifeng Wang, Hongshuai Li, Yongge Lin, Mujie Yang, Shuwen Wang, Jiankun Xu, Mingjun Huang, Jin Fang, Xi Zhuang, Jiaxi Yin, Yuqi Li, Yaqi Chen, Changhong Cheng, Zheng Zhao, Zifeng Zhang, Linfeng Ke, Guolin |
| contents | Recent breakthroughs in Large Language Models (LLMs) have revolutionized scientific literature analysis. However, existing benchmarks fail to adequately evaluate the proficiency of LLMs in this domain, particularly in scenarios requiring higher-level abilities beyond mere memorization and the handling of multimodal data. In response to this gap, we introduce SciAssess, a benchmark specifically designed for the comprehensive evaluation of LLMs in scientific literature analysis. It aims to thoroughly assess the efficacy of LLMs by evaluating their capabilities in Memorization (L1), Comprehension (L2), and Analysis \& Reasoning (L3). It encompasses a variety of tasks drawn from diverse scientific fields, including biology, chemistry, material, and medicine. To ensure the reliability of SciAssess, rigorous quality control measures have been implemented, ensuring accuracy, anonymization, and compliance with copyright standards. SciAssess evaluates 11 LLMs, highlighting their strengths and areas for improvement. We hope this evaluation supports the ongoing development of LLM applications in scientific literature analysis. SciAssess and its resources are available at \url{https://github.com/sci-assess/SciAssess}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01976 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis Cai, Hengxing Cai, Xiaochen Chang, Junhan Li, Sihang Yao, Lin Wang, Changxin Gao, Zhifeng Wang, Hongshuai Li, Yongge Lin, Mujie Yang, Shuwen Wang, Jiankun Xu, Mingjun Huang, Jin Fang, Xi Zhuang, Jiaxi Yin, Yuqi Li, Yaqi Chen, Changhong Cheng, Zheng Zhao, Zifeng Zhang, Linfeng Ke, Guolin Computation and Language Recent breakthroughs in Large Language Models (LLMs) have revolutionized scientific literature analysis. However, existing benchmarks fail to adequately evaluate the proficiency of LLMs in this domain, particularly in scenarios requiring higher-level abilities beyond mere memorization and the handling of multimodal data. In response to this gap, we introduce SciAssess, a benchmark specifically designed for the comprehensive evaluation of LLMs in scientific literature analysis. It aims to thoroughly assess the efficacy of LLMs by evaluating their capabilities in Memorization (L1), Comprehension (L2), and Analysis \& Reasoning (L3). It encompasses a variety of tasks drawn from diverse scientific fields, including biology, chemistry, material, and medicine. To ensure the reliability of SciAssess, rigorous quality control measures have been implemented, ensuring accuracy, anonymization, and compliance with copyright standards. SciAssess evaluates 11 LLMs, highlighting their strengths and areas for improvement. We hope this evaluation supports the ongoing development of LLM applications in scientific literature analysis. SciAssess and its resources are available at \url{https://github.com/sci-assess/SciAssess}. |
| title | SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2403.01976 |