@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/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations
%A Yang, Hao
%A Lu, Hongyuan
%A Zeng, Xinhua
%A Liu, Yang
%A Zhang, Xiang
%A Yang, Haoran
%A Zhang, Yumeng
%A Huang, Shan
%A Wei, Yiran
%A Lam, Wai
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F yang-etal-2025-stephanie
%X 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.
%R 10.18653/v1/2025.findings-naacl.8
%U https://aclanthology.org/2025.findings-naacl.8/
%U https://doi.org/10.18653/v1/2025.findings-naacl.8
%P 153-166
Markdown (Informal)
[Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations](https://aclanthology.org/2025.findings-naacl.8/) (Yang et al., Findings 2025)
ACL
- Hao Yang, Hongyuan Lu, Xinhua Zeng, Yang Liu, Xiang Zhang, Haoran Yang, Yumeng Zhang, Shan Huang, Yiran Wei, and Wai Lam. 2025. Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 153–166, Albuquerque, New Mexico. Association for Computational Linguistics.