详细信息
- 来源站点
- ArXiv CS.CL
- 作者
- Yuyang Liu, Liuzhenghao Lv, Xiancheng Zhang, Jingya Wang Li Yuan, Yonghong Tian
- 文章类型
- PAPER
- 语言
- en
- 发布日期
- 2026-07-28
摘要
arXiv:2505.07889v4 Announce Type: replace Abstract: The realization of autonomous scientific experimentation is currently limited by LLMs' struggle to grasp the strict procedural logic and accuracy required by biological protocols. To address this fundamental challenge, we present \textbf{BioProBench}, a comprehensive resource for procedural reasoning in biology. BioProBench is grounded in \textbf{BioProCorpus}, a foundational collection of 22,413 human-written protocols. From this corpus, we systematically constructed a dataset of 523,784 task instances, offering both a large-scale training resource and a rigorous benchmark with novel metrics. Evaluating 10 mainstream LLMs, we find that while general comprehension is high, performance drops significantly on tasks demanding deep reasoning, quantitative precision, and safety awareness.