  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.
