回复"什么时候大型语言模型跨越了界限:在医疗保健中"推理"红色团队"
1Stanford School of Medicine, Stanford, CA, USA. roxanad@stanford.edu.
NPJ digital medicine
|November 12, 2025
概括
医疗保健中大型语言模型的未来红色团队必须评估内部推理,而不仅仅是最终的答案. 扩大审计范围,包括推理质量和伦理场景,将提高大型语言模型的安全性.
科学领域:
- 人工智能的人工智能
- 医疗保健技术 技术 医疗保健 技术
- 医学伦理 医学伦理
背景情况:
- 医疗保健中大型语言模型 (LLM) 的当前红色组合主要集中在最终产品上.
- 这种方法忽略了LLMs内部推理过程中的关键缺陷.
- 专注于推理的模型引入了需要具体评估的新风险.
研究的目的:
- 为医疗保健中的大型语言模型提倡加强红色团队合作策略.
- 强调需要评估LLMs的内部推理质量.
- 提议扩大红色团队,包括认知偏见和道德考虑.
主要方法:
- 批判性地分析当前大型语言模型红色团队在医疗保健中的局限性.
- 强调评估内部推理途径的重要性.
- 建议将各种伦理场景纳入审计框架.
主要成果:
- 仅分析大型语言模型的最终答案,就无法检测推理错误.
- 从先进的大型语言模型的推理能力中出现了新的风险.
- 目前的红色团队框架对于全面的大型语言模型评估是不够的.
结论:
- 医疗保健中大型语言模型的未来红色团队必须超越最终的答案,审视推理.
- 结合推理质量,认知偏见和伦理场景的评估,将加强审计框架.
- 需要采取更强大的方法,以确保在医疗应用中安全有效地部署大型语言模型.
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