Recent advancements in large language models (LLMs) have shown promise in generating psychotherapeutic dialogues, particularly in the context of motivational interviewing (MI). However, the inherent lack of transparency in LLM outputs presents significant challenges given the sensitive nature of psychotherapy. Applying MI strategies, a set of MI skills, to generate more controllable therapeutic-adherent conversations with explainability provides a possible solution. In this work, we explore the alignment of LLMs with MI strategies by first prompting the LLMs to predict the appropriate strategies as reasoning and then utilizing these strategies to guide the subsequent dialogue generation. We seek to investigate whether such alignment leads to more controllable and explainable generations. Multiple experiments including automatic and human evaluations are conducted to validate the effectiveness of MI strategies in aligning psychotherapy dialogue generation. 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，大型语言模型（LLMs）在生成心理治疗对话方面显示出潜力，特别是在动机性访谈（MI）的背景下。然而，鉴于心理治疗的敏感性，LLM输出固有的不透明性带来了重大挑战。应用MI策略（一组MI技能）来生成更具可控性、符合治疗原则且可解释的对话，提供了一种可能的解决方案。在本工作中，我们通过首先提示LLMs预测适当的策略作为推理，然后利用这些策略指导后续的对话生成，来探索LLMs与MI策略的契合性。我们旨在研究这种契合是否会导致更具可控性和可解释性的生成结果。我们进行了包括自动评估和人工评估在内的多项实验，以验证MI策略在契合心理治疗对话生成方面的有效性。我们的研究结果证明了LLMs在生成策略契合的对话方面的潜力，并为心理治疗场景中的实际应用指明了方向。