AI 中文总结
针对大语言模型生物滥用问题，引入BioTIER基准，将生物内容分三个风险集，含542个专家策划提示及元数据，可隔离控制灾难性风险信息，确保生物科学知识获取，助力针对性生物风险缓解。
AI 中文摘要
随着大语言模型能力不断增强，对其可能助力生物滥用的担忧与日俱增。模型生态系统中安全优先级各异，有些模型随意提供可能被滥用的高风险信息，有些则拒绝良性科学内容，这源于缺乏针对性缓解措施来区分危险信息与广泛科学内容。为解决此问题，我们引入BioTIER，一个旨在实现更有针对性生物风险缓解的基准。BioTIER将生物内容分为三个风险集：灾难避免（CA）、生物医学DURC（BD）和相关生物学（RB）。该基准由542个专家策划的提示及丰富元数据组成，以支持差异化访问策略。我们发布BioTIER以帮助隔离和控制可能因滥用引发灾难性风险的少量信息，同时确保获取推进生物科学所需的大量知识。
英文摘要
As large language models become increasingly capable, concerns about their potential to assist with biological misuse continue to grow. Prioritization of safety differs across the model ecosystem, with some models freely providing high-risk information that could be misused, and others refusing benign scientific content, potentially hindering legitimate research. Both failures stem from a lack of targeted mitigation to distinguish the most dangerous information from broader scientific content. To address this, we introduce BioTIER (Biological Targeted Information for Exclusion and Refusal), a benchmark designed to enable more targeted biological risk mitigation. BioTIER organizes biological content into three risk sets: Catastrophe Avoidance (CA), Biomedical DURC (BD) and Related Biology (RB). These sets represent a spectrum from extremely narrow high-risk topics to a broad range of benign and beneficial biological knowledge. The benchmark consists of 542 expert-curated prompts with rich associated metadata to support differentiated access policies. We release BioTIER to aid in isolating and gating the tiny fraction of information that could engender catastrophic risk from misuse, while ensuring access to the vast wealth of knowledge that is essential for advancing biological science.
Comments52 pages, 9 main figures, 9 supplementary figures, 1 main table, 9 supplementary tables
