Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning
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...
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Main Authors | , , , , , , |
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Format | Journal Article |
Language | English |
Published |
23.03.2024
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Subjects | |
Online Access | Get full text |
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Summary: | 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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DOI: | 10.48550/arxiv.2403.15737 |