Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning

• Main Authors: Xie, Zhouhang, Majumder, Bodhisattwa Prasad, Zhao, Mengjie, Maeda, Yoshinori, Yamada, Keiichi, Wakaki, Hiromi, McAuley, Julian
• Format: Journal Article
• Language: English
• Published: 23.03.2024
• Subjects: Computer Science - Computation and Language
• DOI: 10.48550/arxiv.2403.15737

We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes: Motivational Interviewing. Addressing such a task requires a system that can infer \textit{how} to motivate a user effectively. We propose DIIT, a framework that is capable of learning and applying conversation strategies in the form of natural language inductive rules from expert demonstrations. Automatic and human evaluation on instruction-following large language models show natural language strategy descriptions discovered by DIIR can improve active listening skills, reduce unsolicited advice, and promote more collaborative and less authoritative responses, outperforming various demonstration utilization methods.