小模型coTherapist可辅助心理治疗，表现接近专家水平。
coTherapist: A Behavior-Aligned Small Language Model to Support Mental Healthcare Experts
- 用领域微调+检索增强+代理推理，让小模型模仿治疗师核心能力。
- 在临床问答中，响应相关性与临床准确性优于现有基线模型。
- 专家评测确认其有高共情、可信且安全，适合临床辅助场景。
心理医疗服务面临人力短缺与需求上升的双重压力，亟需智能系统辅助专业人员。本文提出coTherapist，一个基于小型语言模型的统一框架，通过领域特定微调、检索增强和代理推理，模拟核心治疗能力。在临床问题评估中，coTherapist生成的回应比现有基线更相关、更具临床依据。借助新型T-BARS量表与心理特质分析，验证其具备高度共情力与治疗师一致的人格特征。此外，领域专家的人工评估证实，coTherapist输出准确、可信且安全。该系统已在临床环境中部署并经专家测试。综合结果表明，经过精心设计的小型模型可实现类专家行为，为数字心理健康工具提供可扩展路径。
原文摘要 · Abstract (English)
Access to mental healthcare is increasingly strained by workforce shortages and rising demand, motivating the development of intelligent systems that can support mental healthcare experts. We introduce coTherapist, a unified framework utilizing a small language model to emulate core therapeutic competencies through domain-specific fine-tuning, retrieval augmentation, and agentic reasoning. Evaluation on clinical queries demonstrates that coTherapist generates more relevant and clinically grounded responses than contemporary baselines. Using our novel T-BARS rubric and psychometric profiling, we confirm coTherapist exhibits high empathy and therapist-consistent personality traits. Furthermore, human evaluation by domain experts validates that coTherapist delivers accurate, trustworthy, and safe responses. coTherapist was deployed and tested by clinical experts. Collectively, these findings demonstrate that small models can be engineered to exhibit expert-like behavior, offering a scalable pathway for digital mental health tools.
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