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一个统计框架,用于评估大型语言模型的可重复性和可重复性.

Cathy Shyr1,2,3, Boyu Ren4, Chih-Yuan Hsu2

  • 1Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, 37203, TN, USA.

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概括

我们开发了一个统计框架来衡量大语言模型 (LLM) 在医学中的可靠性. 我们的研究结果表明,LLM一致性不能保证诊断准确性,强调了医疗AI中需要标准化的可靠性指标的需要.

关键词:
人工智能的人工智能是人工智能.诊断推理 诊断推理 诊断推理大型语言模型可以重复的可重复性.可复制性的可复制性

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科学领域:

  • 人工智能在医学中的应用
  • 计算语言学 计算语言学
  • 医疗信息学 医疗信息学

背景情况:

  • 大型语言模型 (LLM) 在医学中具有潜力,但由于随机文本生成,它们的可靠性是一个主要问题.
  • 士学位成果的变化可能会影响医疗应用,但缺乏评估这一点的标准化指标.

研究的目的:

  • 提出和验证一个统计框架,用于系统量化医学应用中LLM的可靠性.
  • 引入可重复性和可重复性的指标,评估LLM响应的语义一致性和内部稳定性.

主要方法:

  • 开发了一个测量LLM重复性 (相同条件) 和可重复性 (不同的条件) 的框架.
  • 评估了LLM响应的语义一致性和内部稳定性.
  • 将框架应用于医学推理任务,使用美国医学执照考试 (USMLE) 问题和未诊断疾病网络 (UDN) 案例.

主要成果:

  • 与标准化USMLE问题相比,LLM答案对复杂的UDN罕见疾病病例的变化较小.
  • 可重复性和可重复性指标与诊断准确性没有相关性.
  • 该研究强调,一致的LLM输出并不等同于准确的输出.

结论:

  • 拟议的框架提供了一个系统的方法来量化医学LLM可靠性.
  • 这种量化对于LLMs在临床实践和生物医学研究中安全有效地整合至关重要.
  • 确保LLM可靠性对于推进人工智能驱动的医疗保健解决方案至关重要.