将行为科学注入到大型语言模型中,用于活动指导
Narayan Hegde1, Madhurima Vardhan1, Deepak Nathani1
1Google Research, Bangalore, India.
PLOS digital health
|April 2, 2024
概括
这项研究将行为科学整合到健康指导的大型语言模型 (LLM) 中. 启动和重新排名LLM响应显著改善了模拟指导对话中的同情和可操作性.
科学领域:
- 人工智能的人工智能
- 行为科学 行为科学
- 卫生教练健康教练
背景情况:
- 大型语言模型 (LLM) 显示了面向任务的对话的潜力.
- 在健康和健身教练的LLM应用仍然未被充分探索.
- 行为科学框架,如COM-B (能力,机会,动机),提供结构化的方法来改变行为.
研究的目的:
- 将行为科学原则纳入健康指导的法学士课程.
- 加强LLM驱动的辅导干预,以实现持续的行为改变.
- 探索知识输入技术,以提高法学士辅导能力.
主要方法:
- 使用了两种知识输入技术:基于COM-B的教练消息启动和对话重新排名.
- 在LLMs和研究小组成员之间进行模拟的辅导对话.
- 使用人类评分器评估对话质量,专注于同情和可操作性.
主要成果:
- 与未启动的对话相比,启动的LLM对话在同理心和可操作性方面获得了显著更高的评分.
- 对话重新排名进一步增强了LLM的响应,显示了可操作性和同理心的显著提升.
- 这些发现证明了行为科学原则在LLM辅导中的有效性.
结论:
- 行为科学框架可以有效地注入自动化对话代理.
- 基于LLM的辅导干预可以变得更有原则和更有效.
- 这项工作提出了通过行为科学增强健康和健身指导的LLM概念的证明.
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