在临床人工智能应用的大型语言模型中通过微调和促进提高公平性
Pradeep Kamboj1, Shailender Kumar2, Vikram Goyal3
1Department of Computer Science and Engineering, Delhi Technological University; kamboj.pradeep@gmail.com.
Journal of visualized experiments : JoVE
|January 19, 2026
概括
大型语言模型 (LLM) 在医疗保健等敏感领域显示偏见. 偏差提示和微调显著减少了这些偏差,提高了临床应用的公平性和可解释性.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 大型语言模型 (LLM) 在医疗保健等敏感领域面临道德和技术挑战.
- 从培训数据中得出的LLM中的偏见可能导致不公平,不可泛化或有害的输出,阻碍临床适用性和监管合规性.
研究的目的:
- 为了评估微调的LLM中偏差抑制的有效性,使用 debiased提示.
- 评估跨性别,种族,职业和宗教类别的公平性和解释性.
主要方法:
- 手动策划各种推理数据集的手动策划.
- 为三个开源LLM (Llama2-7B,Mistral-7B,Dolly-7B) 开发了十二个快速变体 (六个是无基础的).
- 使用偏差评分指标对公平性和可解释性进行评估.
主要成果:
- 偏差提示显然可以减少LLM输出中的偏差.
- 微调模型进一步增强了偏见抑制,提高了公平性.
- 较低的偏见分数表明更好的公平性和解释性.
结论:
- 偏差提示和模型微调是缓解LLM偏差的有效策略.
- 稳定性和持续的道德审查对于在医疗照顾环境中部署可信的LLM至关重要,例如医学成像和EHR维护.
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