Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning
<|reference_start|>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 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.<|reference_end|>
@article{xie2024few-shot,
    title={Few-shot Dialogue Strategy Learning for Motivational Interviewing via
  Inductive Reasoning},
    author={Zhouhang Xie, Bodhisattwa Prasad Majumder, Mengjie Zhao, Yoshinori
  Maeda, Keiichi Yamada, Hiromi Wakaki, Julian McAuley},
    journal={arXiv preprint arXiv:2403.15737},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15737},
    primaryClass={cs.CL}
}