Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations
Hao Yang, Hongyuan Lu, Xinhua Zeng, Yang Liu, Xiang Zhang, Haoran Yang, Yumeng Zhang, Shan Huang
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.
BibTeX
@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://aclanthology.org/2025.findings-naacl.8/",
pages = "153--166",
ISBN = "979-8-89176-195-7"
}