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}.
@article{arxiv.2403.01976,
title = {SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis},
author = {Hengxing Cai and Xiaochen Cai and Junhan Chang and Sihang Li and Lin Yao and Changxin Wang and Zhifeng Gao and Hongshuai Wang and Yongge Li and Mujie Lin and Shuwen Yang and Jiankun Wang and Mingjun Xu and Jin Huang and Xi Fang and Jiaxi Zhuang and Yuqi Yin and Yaqi Li and Changhong Chen and Zheng Cheng and Zifeng Zhao and Linfeng Zhang and Guolin Ke},
journal= {arXiv preprint arXiv:2403.01976},
year = {2024}
}