@inproceedings{yang-etal-2025-stephanie,
    title = "Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations",
    author = "Yang, Hao  and
      Lu, Hongyuan  and
      Zeng, Xinhua  and
      Liu, Yang  and
      Zhang, Xiang  and
      Yang, Haoran  and
      Zhang, Yumeng  and
      Huang, Shan  and
      Wei, Yiran  and
      Lam, Wai",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.8/",
    pages = "153--166",
    ISBN = "979-8-89176-195-7",
    abstract = "In the rapidly evolving field of natural language processing, dialogue systems primarily employ a single-step dialogue paradigm. Although this paradigm is commonly adopted, it lacks the depth and fluidity of human interactions and does not appear natural. We introduce a novel **Step**-by-Step Dialogue Paradigm (Stephanie), designed to mimic the ongoing dynamic nature of human conversations. By employing a dual learning strategy and a further-split post-editing method, we generated and utilized a high-quality step-by-step dialogue dataset to fine-tune existing large language models, enabling them to perform step-by-step dialogues. We thoroughly present Stephanie. Tailored automatic and human evaluations are conducted to assess its effectiveness compared to the traditional single-step dialogue paradigm. We will release code, Stephanie datasets, and Stephanie LLMs to facilitate the future of chatbot eras."
}