ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalness
This work addresses the underexplored problem of conversational AI naturalness and user engagement for improving human-AI communication, though it is incremental as it focuses on analysis rather than new methods.
The study analyzed ChatGPT's behavior in normal and role-play conversations by creating an annotated dataset of human-AI interactions, finding diverse user motives and variable AI naturalness in these settings.
Recent advances in interactive large language models like ChatGPT have revolutionized various domains; however, their behavior in natural and role-play conversation settings remains underexplored. In our study, we address this gap by deeply investigating how ChatGPT behaves during conversations in different settings by analyzing its interactions in both a normal way and a role-play setting. We introduce a novel dataset of broad range of human-AI conversations annotated with user motives and model naturalness to examine (i) how humans engage with the conversational AI model, and (ii) how natural are AI model responses. Our study highlights the diversity of user motives when interacting with ChatGPT and variable AI naturalness, showing not only the nuanced dynamics of natural conversations between humans and AI, but also providing new avenues for improving the effectiveness of human-AI communication.