@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",
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 \textit{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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<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.</abstract>
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%0 Conference Proceedings
%T Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning
%A Xie, Zhouhang
%A Majumder, Bodhisattwa Prasad
%A Zhao, Mengjie
%A Maeda, Yoshinori
%A Yamada, Keiichi
%A Wakaki, Hiromi
%A McAuley, Julian
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F xie-etal-2024-shot-dialogue
%X 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.
%R 10.18653/v1/2024.findings-acl.782
%U https://aclanthology.org/2024.findings-acl.782
%U https://doi.org/10.18653/v1/2024.findings-acl.782
%P 13207-13219
Markdown (Informal)
[Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning](https://aclanthology.org/2024.findings-acl.782) (Xie et al., Findings 2024)
ACL