利用大型语言模型来识别心理治疗成绩单中的微咨询技能
Karin Hammerfald1, Fabian Schmidt2, Vladimir Vlassov2
1Department of Psychology, University of Oslo, Oslo, Norway.
概括
大型语言模型 (LLM) 可以在心理治疗中自动化微咨询技能识别. 微调的GPT-4.1显示了可扩展,自动化编码的潜力,有助于研究和临床实践.
科学领域:
- 心理学 心理学 心理学
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 微咨询技能对于有效的心理治疗至关重要.
- 手动编码这些技能是劳动密集型和昂贵的.
- 需要自动化方法来分析治疗会话记录.
研究的目的:
- 调查大型语言模型 (LLM) 在自动化微咨询技能识别方面的有效性.
- 微调GPT-4.1用于分类治疗师的发言和评估微型咨询技能.
- 评估LLM在分析心理治疗会议成绩单方面的表现.
主要方法:
- 对人类注释的心理治疗成绩单进行微调GPT-4.1.
- 开发一个用于转录预处理,对话细分和监督微调的管道.
- 训练模型对治疗师的言论进行分类,生成解释,并提出替代反应.
主要成果:
- 微调的GPT-4.1模型实现了强大的性能指标 (精度:0.78,F1:0.78).
- 该模型成功地确定了共同的和结构上不同的微型咨询技能.
- 在检测需要理解隐性关系动态的细微技能方面,表现有限.
结论:
- 精心调整的LLM显示出在心理治疗中自动编码微型咨询技能的巨大潜力.
- 这项技术提供了一个可扩展的解决方案,以增强心理治疗研究和临床实践.
- 需要进一步发展,以解决识别微妙,取决于背景的治疗技能的局限性.
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