The therapeutic working alliance is a critical factor in predicting the success of psychotherapy treatment. Traditionally, working alliance assessment relies on questionnaires completed by both therapists and patients. In this paper, we present COMPASS, a novel framework to directly infer the therapeutic working alliance from the natural language used in psychotherapy sessions. Our approach utilizes advanced large language models to analyze transcripts of psychotherapy sessions and compare them with distributed representations of statements in the working alliance inventory. Analyzing a dataset of over 950 sessions covering diverse psychiatric conditions, we demonstrate the effectiveness of our method in microscopically mapping patient-therapist alignment trajectories and providing interpretability for clinical psychiatry and in identifying emerging patterns related to the condition being treated. By employing various neural topic modeling techniques in combination with generative language prompting, we analyze the topical characteristics of different psychiatric conditions and incorporate temporal modeling to capture the evolution of topics at a turn-level resolution. This combined framework enhances the understanding of therapeutic interactions, enabling timely feedback for therapists regarding conversation quality and providing interpretable insights to improve the effectiveness of psychotherapy.

翻译：摘要：治疗工作联盟是预测心理治疗成效的关键因素。传统上，工作联盟评估依赖治疗师与患者双方填写的问卷。本文提出COMPASS这一新型框架，通过心理治疗对话中的自然语言直接推断治疗工作联盟。该方法利用先进大语言模型分析治疗会话文本，并将其与工作联盟量表中的分布式表征进行对比。通过分析覆盖多种精神疾病的950余次治疗会话数据集，我们验证了该方法在微观层面映射医患关系协调轨迹、为临床精神病学提供可解释性，以及识别与疾病治疗相关的新兴模式方面的有效性。通过结合多种神经主题建模技术与生成式语言提示方法，我们分析了不同精神疾病的主题特征，并引入时序建模以轮级精度捕捉主题演变过程。这一复合框架深化了对治疗互动的理解，为治疗师提供对话质量的实时反馈，并通过可解释性洞察提升心理治疗成效。