使用大型语言模型和隐藏的马尔科夫模型评估激励面试质量
Kyungho Lim1,2, Young-Chul Jung3,4, Byung-Hoon Kim5,6,7,8
1Department of Psychiatry, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
BMC psychiatry
|October 2, 2025
概括
本研究引入了一种使用大语言模型 (LLM) 和隐藏马尔科夫模型 (HMM) 的自动化框架,以客观地评估动机面试 (MI) 质量. 该LLM-HMM框架准确地预测会话质量,并揭示了有效的MI中明显的动机状态过渡.
科学领域:
- 行为科学 行为科学
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 激励面试 (MI) 是一种辅导方法,以促进行为改变.
- 传统的MI质量评估是劳动密集型和主观的.
- 建议使用LLM和HMM的自动化框架进行MI评估.
研究的目的:
- 评估一个LLM-HMM框架来预测MI会话质量.
- 在高质量和低质量的MI会议中检查动机状态过渡.
主要方法:
- 分析了40个MI会议,用LLM对客户发言进行分类.
- 使用HMM来建模基于LLM成绩的动机状态过渡.
- 过渡矩阵的量化差异和使用LOOCV评估的预测性能.
主要成果:
- 高质量的MI会议显示了流动的动机状态过渡;低质量的会议表现出持久的抵抗.
- 在会话质量组之间发现过渡矩阵的统计学上显著差异 (p < 0.001).
- 通过LOOCV,LLM-HMM框架在预测MI会话质量方面实现了0.80准确度.
结论:
- 该LLM-HMM框架提供了一个可扩展和客观的替代手动MI质量评估.
- 潜在的未来应用包括实时治疗师支持,培训和预后预测.
- 建议对现场收集的数据进行进一步验证.
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