在大型语言模型研究中建立最佳实践:用于重复提示的应用
Robert J Gallo1,2, Michael Baiocchi3, Thomas R Savage4
1Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, CA 94025, United States.
Journal of the American Medical Informatics Association : JAMIA
|December 10, 2024
概括
在大型语言模型 (LLM) 研究中建立最佳实践至关重要. 忽视重复提示数据中的相关性可能会导致关于模型偏差的错误结论,正如本研究所示.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 科学研究方法学 科学研究方法学
背景情况:
- 大型语言模型 (LLM) 越来越多地用于研究.
- 确保LLM研究的可靠性和有效性至关重要.
- 重复提示是LLM研究中常见的技术,但它的统计含义需要仔细考虑.
研究的目的:
- 强调在大型语言模型研究中建立最佳实践的重要性.
- 为了说明重复提示数据中忽视相关性如何影响研究结果.
- 为了证明在LLM分析中对适当的统计方法的关键需求.
主要方法:
- 利用了以前一项关于在医学抽象同行评审中潜在模型偏差的研究中的数据.
- 比较的统计方法,忽略输出相关性从重复提示随机效应方法,这是它.
- 分析了类内相关系数 (ICC) 来量化集团内部的相关性.
主要成果:
- 在反复提示的情况下,观察到高的群体内相关性 (ICC=0.69).
- 忽视这种相关性导致有效样本大小的100倍以上的通货膨胀.
- 考虑到相关性,研究结果从显著的偏见证据转变为没有偏见证据.
结论:
- 迫切需要建立大型语言模型研究的最佳实践.
- 对LLM研究进行准确的分析需要考虑重复提示所固有的相关性.
- 如果不实施最佳实践,可能会严重影响研究结论的有效性.
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