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在医学证据总结和总结上评估大型语言模型.

Liyan Tang1, Zhaoyi Sun2, Betina Idnay3

  • 1School of Information, The University of Texas at Austin, Austin, TX, USA.

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大型语言模型 (LLM) 在医学总结方面表现有前途,但在事实一致性和确定关键信息方面存在困难,造成错误信息的风险.

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科学领域:

  • 人工智能的人工智能
  • 医疗信息学 医疗信息学
  • 自然语言处理自然语言处理.

背景情况:

  • 大型语言模型 (LLM) 在各个任务中表现出强大的零和少数射击能力.
  • 医疗保健等高风险领域正在探索LLM应用.

研究的目的:

  • 在零射击医学证据总结中系统评估LLM绩效 (GPT-3.5,ChatGPT).
  • 通过在六个临床领域的自动和人体评估来评估总结质量.

主要方法:

  • 对LLM生成的医学摘要进行了自动和人为评估.
  • 定义了基于人类评估的错误类型的分类.
  • 分析了不同临床领域和文本长度的摘要质量.

主要成果:

  • 自动指标显示与人类判断的摘要质量有很弱的相关性.
  • 实际上,LLM产生的摘要与事实不一致,并表现出有问题的确定性水平.
  • 模型难以识别突出信息,较长文本的准确性较低.

结论:

  • 由于可能存在事实错误和错误信息,LLM需要对医学证据总结进行仔细的验证.
  • 人类评估对于评估LLM生成的医学摘要的质量和安全性至关重要.
  • 处理长文本和识别关键信息的局限性需要进一步研究.