Automatic coding patient behaviors is essential to support decision making for psychotherapists during the motivational interviewing (MI), a collaborative communication intervention approach to address psychiatric issues, such as alcohol and drug addiction. While the behavior coding task has rapidly adapted machine learning to predict patient states during the MI sessions, lacking of domain-specific knowledge and overlooking patient-therapist interactions are major challenges in developing and deploying those models in real practice. To encounter those challenges, we introduce the Chain-of-Interaction (CoI) prompting method aiming to contextualize large language models (LLMs) for psychiatric decision support by the dyadic interactions. The CoI prompting approach systematically breaks down the coding task into three key reasoning steps, extract patient engagement, learn therapist question strategies, and integrates dyadic interactions between patients and therapists. This approach enables large language models to leverage the coding scheme, patient state, and domain knowledge for patient behavioral coding. Experiments on real-world datasets can prove the effectiveness and flexibility of our prompting method with multiple state-of-the-art LLMs over existing prompting baselines. We have conducted extensive ablation analysis and demonstrate the critical role of dyadic interactions in applying LLMs for psychotherapy behavior understanding.

翻译：自动编码患者行为对于在动机访谈（MI）中辅助心理治疗师决策至关重要——这是一种针对酒精和药物成瘾等精神问题的协作式沟通干预方法。尽管行为编码任务已快速采用机器学习来预测MI会话中的患者状态，但领域特定知识的缺失以及对患者-治疗师互动的忽视，仍是实际应用中开发和部署这些模型的主要挑战。为应对这些挑战，我们提出交互链（CoI）提示方法，旨在通过二元互动将大语言模型（LLM）语境化以支持精神病学决策。CoI提示方法将编码任务系统性地分解为三个关键推理步骤：提取患者参与度、学习治疗师提问策略，以及整合患者与治疗师之间的二元互动。该方法使大语言模型能够利用编码方案、患者状态和领域知识进行患者行为编码。在真实世界数据集上的实验证明，我们的提示方法在多种最先进的LLM上均优于现有提示基线，具有显著的有效性和灵活性。我们进行了广泛的消融分析，并证明了二元互动在应用大语言模型理解心理治疗行为中的关键作用。