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科学领域:

  • 人工智能
  • 医疗信息学
  • 自然语言处理

背景情况:

  • 大型语言模型 (LLM) 具有先进的功能,但在临床使用中面临着高标准.
  • 目前对LLM医学知识的评估通常依赖于有限的自动化基准.

研究的目的:

  • 推出MultiMedQA,一个全面的衡量标准来评估医疗问答中的LLM.
  • 建立一个人类评估框架,评估LLM响应中的事实性,理解,推理,伤害和偏见.
  • 评估Pathways语言模型 (PaLM) 和Flan-PaLM在MultiMedQA基准上的表现.

主要方法:

  • 开发了MultiMedQA,整合了六个医疗QA数据集和新的HealthSearchQA数据集.
  • 实施了对LLM产生的医疗答案的人类评估协议.
  • 在MultiMedQA上使用各种提示策略评估PaLM和Flan-PaLM.
  • 引入指令提示符调整以适应LLM的领域.

主要成果:

  • 在所有MultiMedQA多选题数据集中,Flan-PaLM实现了最先进的精度,其中MedQA (USMLE样式问题) 的精度为67.6%.
  • 人类评估发现,尽管自动化得分很高,但LLM的表现存在重大差距.
  • 指示提示调整导致Med-PaLM,其表现有所改善,但仍低于临床医生的水平.

结论:

  • 医学LLM的表现会随着尺度和教学提示的调整而提高.
  • 目前的LLM在临床应用方面存在局限性,这凸显了对强有力的评估框架的需求.
  • 进一步发展对于为医疗保健创造安全有效的LLM至关重要.