基于归纳推理的动机性访谈少样本对话策略学习
Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning
AI总结：
针对动机性访谈任务，提出DIIT框架从专家示范中学习自然语言归纳规则作为对话策略，评估表明该方法能有效提升大语言模型的积极倾听技能并减少主动建议，优于多种基线方法。
摘要：
我们考虑构建一个能激励用户采取积极生活方式改变的对话系统任务：Motivational Interviewing（动机性访谈）。解决该任务需要一个能推断出如何有效激励用户的系统。我们提出了DIIT框架，该框架能够从专家示范中学习并应用以自然语言归纳规则形式呈现的对话策略。在指令遵循大语言模型上的自动和人工评估表明，DIIR发现的自然语言策略描述能提升积极倾听技能，减少主动提供的建议，并促进更具协作性且较少权威性的回复，优于多种示范利用方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
