Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations
This work addresses the problem of unnatural conversations in chatbots for users, though it appears incremental as it builds on existing large language models with a new paradigm.
The paper tackles the lack of depth and naturalness in single-step dialogue systems by introducing a step-by-step dialogue paradigm (Stephanie) that mimics human interactions, resulting in improved performance as shown in tailored evaluations.
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.