在长期护理中的大型语言模型中评估性别偏见
1Care Policy and Evaluation Centre, LSE, London, WC2A 2AE, UK. s.w.rickman@lse.ac.uk.
BMC medical informatics and decision making
|August 10, 2025
概括
最先进的大型语言模型 (LLM) 在总结长期护理记录时显示了不同的性别偏见. 谷歌Gemma表现出明显的偏见,淡化了女性的需求,与Llama 3不同.
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.
- 医疗保健信息学 医疗保健信息学
背景情况:
- 大型语言模型 (LLM) 越来越多地用于自动化长期护理中的行政任务,例如总结患者记录.
- 然而,LLM可以延续其培训数据中存在的偏见,这可能会影响医疗保健公平.
研究的目的:
- 评估近期开源LLMs产生的长期护理记录摘要中的性别偏见.
- 具体来说,将Meta的Llama 3和Google Gemma与较旧的基准模型进行比较.
主要方法:
- 生成了617个长期护理记录的性别交换版本.
- 使用Llama 3,Gemma,T5和BART制作了摘要.
- 使用情感分析,词汇频率和主题模式量化反事实性性别偏见.
主要成果:
- 基准模型显示了一些基于性别的变化.
- 拉玛3在总结中没有显著的基于性别的差异.
- 谷歌Gemma表现出最明显的性别偏见,男性摘要更多地关注健康问题,女性的需求被淡化.
结论:
- 在LLM摘要中的性别偏见,特别是低估妇女健康问题,可能导致护理服务分配的差异.
- 虽然LLM提供了管理方面的好处,但它们的性能差异需要严格的偏见评估.
- 该研究为量化评估法学士课程中的性别偏见提供了实际框架.
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