Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning
Zhouhang Xie, Bodhisattwa Prasad Majumder, Mengjie Zhao, Yoshinori Maeda, Keiichi Yamada, Hiromi Wakaki, Julian McAuley
Abstract
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.
BibTeX
@inproceedings{xie-etal-2024-shot-dialogue,
title = "Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning",
author = "Xie, Zhouhang and
Majumder, Bodhisattwa Prasad and
Zhao, Mengjie and
Maeda, Yoshinori and
Yamada, Keiichi and
Wakaki, Hiromi and
McAuley, Julian",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.782/",
doi = "10.18653/v1/2024.findings-acl.782",
pages = "13207--13219"
}