逐步推进：测量与引导大语言模型开展心理治疗的方式

中文摘要 AI 辅助

越来越多的用户向大语言模型寻求情感支持，但人们对这些模型实际如何开展心理治疗互动知之甚少。我们提出了包含10种治疗动作的本体：基于MULTI-60清单的紧凑功能类别，通过与5名持牌心理学家开展的标注活动验证，并采用匹配专家一致性的基于评判者的方法扩展规模。将其应用于真实咨询记录和模型主导的会话，我们比较了人类临床医生与一组前沿模型之间的动作分布。模型过度使用询问，比例高达人类的三倍，忽视心理教育，且强烈依赖上下文：它们延续人类临床医生发起的策略，但很少自行发起。将该本体作为一组工具公开，可将与人类动作分布的平均偏差大致减半，并将回合级与人类治疗师的对齐度提高7至9个百分点，且无需任何微调。

英文摘要

Users increasingly turn to large language models for emotional support, yet little is known about how these models actually conduct a psychotherapy interaction. We introduce an ontology of ten therapeutic moves: compact, function-based categories grounded in the MULTI-60 inventory, validated through an annotation campaign with five licensed psychologists, and scaled with a judge-based approach that matches expert agreement. Applying it to real counseling transcripts and model-led sessions, we compare the move distributions between human clinicians and a panel of frontier models. Models over-use inquiry at up to three times the human rate, neglect psychoeducation, and are strongly context-anchored: they carry forward strategies initiated by a human clinician but rarely initiate them themselves. Exposing the ontology as a set of tools roughly halves the mean deviation from the human move distribution and improves turn-level alignment with human therapist by 7-9 percentage points, without any fine-tuning.

发表机构

• Sword Health
• Yale University（耶鲁大学）
• Instituto Superior Técnico（高等技术学院）

机构由 AI 辅助整理，请以论文原文为准。