Recently, there has been growing interest in using Large Language Models
(LLMs) for scientific research. Numerous benchmarks have been proposed to
evaluate the ability of LLMs for scientific research. However, current
benchmarks are mostly based on pre-collected objective questions. This design
suffers from data leakage problem and lacks the evaluation of subjective Q/A
ability. In this paper, we propose SciEval, a comprehensive and
multi-disciplinary evaluation benchmark to address these issues. Based on
Bloom's taxonomy, SciEval covers four dimensions to systematically evaluate
scientific research ability. In particular, we design a "dynamic" subset based
on scientific principles to prevent evaluation from potential data leakage.
Both objective and subjective questions are included in SciEval. These
characteristics make SciEval a more effective benchmark for scientific research
ability evaluation of LLMs. Comprehensive experiments on most advanced LLMs
show that, although GPT-4 achieves SOTA performance compared to other LLMs,
there is still substantial room for improvement, especially for dynamic
questions. The data and codes are now publicly available.

这篇论文提出了 SciEval 基准评估体系，以解决现有预先收集客观问题的数据泄露问题和缺乏主观问答能力评估的问题。SciEval 基于 Bloom 的认知分类学，覆盖了四个维度，系统评估科学研究能力。研究者进行了全面的实验证明，尽管 GPT-4 在与其他 LLMs 相比取得了最先进的表现，但仍有很大的改进空间，特别是在动态问题方面。数据和代码现已公开。