Yoshinori Maeda
2024
Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning
Zhouhang Xie
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Bodhisattwa Prasad Majumder
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Mengjie Zhao
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Yoshinori Maeda
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Keiichi Yamada
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Hiromi Wakaki
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Julian McAuley
Findings of the Association for Computational Linguistics: ACL 2024
We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes, Motivational Interviewing (MI). Addressing such a task requires a system that could infer how to motivate the user effectively. We propose DIIR, 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 strategies descriptions discovered by DIIR can improve active listening skills, reduce unsolicited advice, and promote more collaborative and less authoritative conversations, outperforming in-context demonstrations that are over 50 times longer.
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Co-authors
- Zhouhang Xie 1
- Bodhisattwa Prasad Majumder 1
- Mengjie Zhao 1
- Keiichi Yamada 1
- Hiromi Wakaki 1
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