调整大语言模型用于认知行为疗法:一个概念验证研究
Yejin Kim1, Chi-Hyun Choi2, Selin Cho3
1Department of Statistics and Data Science, Yonsei University, Seoul, Republic of Korea.
Frontiers in psychiatry
|September 22, 2025
概括
本研究介绍了LLM4CBT,这是用于认知行为疗法 (CBT) 的大型语言模型 (LLM). LLM4CBT模仿专家治疗师,唤起患者的想法,并适应患者的参与,在精神卫生保健中推进AI.
科学领域:
- 人工智能在心理健康中的作用
- 计算精神病学是一种计算精神病学.
- 在治疗中使用自然语言处理.
背景情况:
- 大型语言模型 (LLM) 对自然语言生成有希望,但在临床心理治疗对齐方面面临挑战.
- 现有的LLM努力坚持有效的心理健康干预所需的专业治疗策略.
研究的目的:
- 推出LLM4CBT,一个专门为认知行为疗法 (CBT) 设计的新型LLM.
- 评估LLM4CBT与专家治疗师行为保持一致并引起治疗反应的能力.
- 评估LLM4CBT在患者互动中的适应性,特别是对于那些在参与方面扎的人.
主要方法:
- 开发LLM4CBT,一个针对认知行为疗法 (CBT) 原则的LLM.
- 使用现实世界的对话数据进行实验评估,以将LLM4CBT与人类专家治疗师进行比较.
- 在模拟的对话数据上进行测试,以评估模型在引起自动思维和调整对话速度方面的能力.
主要成果:
- LLM4CBT在真实世界的数据上与专家人类治疗师保持一致,显示出比其他LLM更可取的治疗行为.
- 实验表明,LLM4CBT在模拟的患者对话中有效地引起无意识的自动思想.
- 该模型显示了暂停并等待患者参与的能力,改善了难以参与的患者的互动.
结论:
- LLM4CBT为开发人工智能驱动的CBT治疗师提供了一种可行的方法.
- 将LLM与特定的治疗指令相结合,是它们在精神卫生保健中的有效应用的关键.
- 这些发现表明,在心理治疗中,基于LLM的干预措施具有显著的潜力.
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