应用统一的健康大语言模型评估框架到篮子中的消息回复:桥梁定性和定量评估
Chuan Hong1, Anand Chowdhury2, Anthony D Sorrentino2
1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, United States.
概括
本研究引入了一个统一的框架,用于评估医疗保健中的大型语言模型 (LLM),结合定性和定量方法. 定量指标在使法学士评估更具可扩展性和客观性方面表现有前途,补充了人类的判断力.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 正通过先进的语言处理来改变医疗保健.
- 目前的LLM评估严重依赖于耗时,有偏见的人类评估.
- 对于LLM评估而言,急需可扩展,客观的定量指标.
研究的目的:
- 提出和验证医疗保健中LLM的统一评估框架.
- 将定性的人类评估与定量指标相结合.
- 为了实现可扩展和系统的LLM绩效评估.
主要方法:
- 制定了一个框架,将评估方面 (语言质量,效率,可靠性,有用性) 映射到定性和定量措施中.
- 应用了框架来经验性地评估一个由LLM驱动的患者消息回复系统 (Epic In-Basket).
- 与定量指标相关联的临床医生决策.
主要成果:
- 人工智能生成的答案表现出高流性,清晰性和低毒性,但存在连贯性和完整性问题.
- 临床医生采用人工智能草案与定量指标有很强的相关性.
- 定量指标显示了减少人力资源和提高评估可扩展性的潜力.
结论:
- 整合定性和定量方法的统一框架提高了医疗保健中的LLM评估可靠性和可扩展性.
- 自动化指标简化了评估,但需要与人类判断对齐以进行上下文解释.
- 完善评估策略和跨学科合作对于保持准确性,道德和合规性至关重要,因为LLM的使用正在扩大.
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