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.- Anthology ID:
- 2024.lrec-main.278
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 3133–3145
- Language:
- URL:
- https://preview.aclanthology.org/remove-affiliations/2024.lrec-main.278/
- DOI:
- Cite (ACL):
- Yufei Tao, Ameeta Agrawal, Judit Dombi, Tetyana Sydorenko, and Jung In Lee. 2024. ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalness. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 3133–3145, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalness (Tao et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/remove-affiliations/2024.lrec-main.278.pdf