ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalness
Yufei Tao, Ameeta Agrawal, Judit Dombi, Tetyana Sydorenko, Jung In Lee
Abstract
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.
BibTeX
@inproceedings{tao-etal-2024-chatgpt,
title = "{C}hat{GPT} Role-play Dataset: Analysis of User Motives and Model Naturalness",
author = "Tao, Yufei and
Agrawal, Ameeta and
Dombi, Judit and
Sydorenko, Tetyana and
Lee, Jung In",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.278/",
pages = "3133--3145"
}