Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations
<|reference_start|>Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations: 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.<|reference_end|>
@article{yang2024stephanie:,
    title={Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in
  Social Conversations},
    author={Hao Yang, Hongyuan Lu, Xinhua Zeng, Yang Liu, Xiang Zhang, Haoran
  Yang, Yumeng Zhang, Shan Huang, Yiran Wei, Wai Lam},
    journal={arXiv preprint arXiv:2407.04093},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04093},
    primaryClass={cs.CL}
}