In the rapidly evolving field of natural language processing, dialogue systems primarily employ a single-step dialogue paradigm. Although this paradigm is efficient, it lacks the depth and fluidity of human interactions and does not appear natural. We introduce a novel \textbf{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.
@article{arxiv.2407.04093,
title = {Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations},
author = {Hao Yang and Hongyuan Lu and Xinhua Zeng and Yang Liu and Xiang Zhang and Haoran Yang and Yumeng Zhang and Shan Huang and Yiran Wei and Wai Lam},
journal= {arXiv preprint arXiv:2407.04093},
year = {2024}
}