标题： 对医疗领域大型语言模型的红队测试 显示英文标题

标题： Red Teaming Large Language Models for Healthcare

摘要： 我们介绍了机器学习用于医疗会议（2024）的会前研讨会的设计过程和发现，该研讨会题为“针对医疗领域的大型语言模型进行红队测试”，于2024年8月15日举行。与会者由计算和临床专业知识混合组成，尝试发现漏洞——即大型语言模型（LLM）可能产生可能导致临床伤害的响应的真实临床提示。与临床医生进行红队测试可以识别出那些缺乏临床专业知识的LLM开发人员可能无法认识到的LLM漏洞。我们报告了发现的漏洞，对它们进行了分类，并展示了评估所有提供的LLMs中漏洞结果的复制研究。 显示英文摘要

摘要： We present the design process and findings of the pre-conference workshop at the Machine Learning for Healthcare Conference (2024) entitled Red Teaming Large Language Models for Healthcare, which took place on August 15, 2024. Conference participants, comprising a mix of computational and clinical expertise, attempted to discover vulnerabilities -- realistic clinical prompts for which a large language model (LLM) outputs a response that could cause clinical harm. Red-teaming with clinicians enables the identification of LLM vulnerabilities that may not be recognised by LLM developers lacking clinical expertise. We report the vulnerabilities found, categorise them, and present the results of a replication study assessing the vulnerabilities across all LLMs provided.