Recent advancements in large language models (LLMs) have shown promise in generating psychotherapeutic dialogues, especially in Motivational Interviewing (MI). However, how to employ strategies, a set of motivational interviewing (MI) skills, to generate therapeutic-adherent conversations with explainability is underexplored. We propose an approach called strategy-aware dialogue generation with Chain-of-Strategy (CoS) planning, which first predicts MI strategies as reasoning and utilizes these strategies to guide the subsequent dialogue generation. It brings the potential for controllable and explainable generation in psychotherapy by aligning the generated MI dialogues with therapeutic strategies. Extensive experiments including automatic and human evaluations are conducted to validate the effectiveness of the MI strategy. Our findings demonstrate the potential of LLMs in producing strategically aligned dialogues and suggest directions for practical applications in psychotherapeutic settings.

翻译：近年来，大语言模型（LLMs）的进展在生成心理治疗对话方面展现出潜力，尤其是在动机性访谈（MI）领域。然而，如何运用策略——即一套动机性访谈技能——来生成符合治疗原则且具有可解释性的对话，目前尚未得到充分探索。我们提出了一种称为“策略感知对话生成”的方法，该方法结合了策略链（CoS）规划。它首先将MI策略预测为推理过程，并利用这些策略来指导后续的对话生成。通过使生成的MI对话与治疗策略对齐，该方法为心理治疗领域带来了可控且可解释的生成潜力。我们进行了包括自动评估和人工评估在内的大量实验，以验证MI策略的有效性。我们的研究结果证明了LLMs在生成与策略对齐的对话方面的潜力，并为心理治疗场景中的实际应用指明了方向。