<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="xie-etal-2024-shot-dialogue">
    <titleInfo>
        <title>Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Zhouhang</namePart>
        <namePart type="family">Xie</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Bodhisattwa</namePart>
        <namePart type="given">Prasad</namePart>
        <namePart type="family">Majumder</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mengjie</namePart>
        <namePart type="family">Zhao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yoshinori</namePart>
        <namePart type="family">Maeda</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Keiichi</namePart>
        <namePart type="family">Yamada</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hiromi</namePart>
        <namePart type="family">Wakaki</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Julian</namePart>
        <namePart type="family">McAuley</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics ACL 2024</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Lun-Wei</namePart>
            <namePart type="family">Ku</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Andre</namePart>
            <namePart type="family">Martins</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Vivek</namePart>
            <namePart type="family">Srikumar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Bangkok, Thailand and virtual meeting</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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>
    <identifier type="citekey">xie-etal-2024-shot-dialogue</identifier>
    <location>
        <url>https://aclanthology.org/2024.findings-acl.782</url>
    </location>
    <part>
        <date>2024-08</date>
        <extent unit="page">
            <start>13207</start>
            <end>13219</end>
        </extent>
    </part>
</mods>
</modsCollection>

%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 and virtual meeting
%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.
%U https://aclanthology.org/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)

• Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning (Xie et al., Findings 2024)

ACL

• Zhouhang Xie, Bodhisattwa Prasad Majumder, Mengjie Zhao, Yoshinori Maeda, Keiichi Yamada, Hiromi Wakaki, and Julian McAuley. 2024. Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning. In Findings of the Association for Computational Linguistics ACL 2024, pages 13207–13219, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.