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

• URL: http://arxiv.org/abs/2403.15737v1
• Date: Sat, 23 Mar 2024 06:03:37 GMT
• Title: Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning
• Authors: Zhouhang Xie, Bodhisattwa Prasad Majumder, Mengjie Zhao, Yoshinori Maeda, Keiichi Yamada, Hiromi Wakaki, Julian McAuley,
• Abstract summary: We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes: Motivational Interviewing. 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.
• Score: 21.078032718892498
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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