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自动评分,以有效评估大型语言模型的双重用途生物能力

Bria Persaud, Ying-Chiang Jeffrey Lee, Jordan Despanie

Rand health quarterly

|October 1, 2025

概括

大型语言模型 (LLM) 显示生物知识,并可以生成实验室协议. 开发了一种自动化方法来评估LLM对生物风险评估的能力.

科学领域:

生物安全是生物安全.
人工智能的人工智能
分子生物学分子生物学

背景情况:

大型语言模型 (LLM) 表明生物知识和推理越来越多.
有关LLM促进生物风险的潜力存在担忧.

研究的目的:

评估LLM在生成与生物威胁创造相关的实验室协议方面的能力.
引入一个可扩展的,自动化的方法来评估LLM在这个领域的表现.

主要方法:

在基于知识的问题和常见实验室技术的协议生成上评估了LLM.
开发了一种自动化,系统的评估方法,以克服基于专家的评估的局限性.

主要成果:

法学士具有重要的生物科学知识.
自动化方法为评估生物安全风险的LLM能力提供了一个可扩展的方法.

结论:

士证明了对生物知识和协议生成的熟练程度.
开发的自动化方法对于在生物威胁的背景下评估AI风险至关重要.

关键词:

生物技术是生物技术.生物恐怖主义生物恐怖主义机器学习机器学习

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