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
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.