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.

翻译：在自然语言处理快速发展的领域中，对话系统主要采用单步对话范式。尽管该范式效率较高，但缺乏人类交互的深度与流畅性，且显得不够自然。我们提出了一种新颖的渐进式对话范式（Stephanie），旨在模拟人类对话持续动态变化的特性。通过采用双重学习策略和进一步分割的后编辑方法，我们生成并利用了一个高质量的渐进式对话数据集，对现有大语言模型进行微调，使其能够执行渐进式对话。我们全面介绍了Stephanie。针对性地进行了自动评估和人工评估，以检验其相较于传统单步对话范式的有效性。我们将发布代码、Stephanie数据集及Stephanie大语言模型，以促进未来聊天机器人时代的发展。