ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalness
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Abstract (arXiv)
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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 20 of its 62 references that are in the map. First posted on arXiv 26 Mar 2024.
Builds on
20 of 62 references.
References · 20
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 888
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- 2023Better Zero-Shot Reasoning with Role-Play Promptingcited by 220
- 2023On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspectivecited by 208
- 2023Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Studycited by 203
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- 2024Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignmentcited by 104
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- 2022ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reportscited by 100
8 more
- 2022ChatGPT: The End of Online Exam Integrity?cited by 88
- 2023ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Textcited by 84
- 2022In conversation with Artificial Intelligence: aligning language models with human valuescited by 61
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Built on by
Citing papers per year: 2024: 1; 2025: 2; 2026: 4.
7 papers.
- 2025Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactionscited by 8
- 2025GuideLLM: Exploring LLM-Guided Conversation with Applications in Autobiography Interviewingcited by 1
- 2026"Excuse me, may I say something..." CoLabScience, A Proactive AI Assistant for Biomedical Discovery and LLM-Expert Collaborationscited by 1
- 2024Generative AI, Pragmatics, and Authenticity in Second Language Learningcited by 0
- 2026Profiling learners' affective engagement: Emotion AI, intercultural pragmatics, and language learningcited by 0
- 2026DRInQ: Evaluating Conversational Implicature with Controlled Context Variationcited by 0
- 2026Deep Persona: A Psychologically Grounded Architecture and Evaluation Framework for Role-Playing Agents and Simulationscited by 0
- 2026Deep Persona: A Psychologically Grounded Architecture and Evaluation Framework for Role-Playing Agents and Simulations6 Sep 2026
- 2026DRInQ: Evaluating Conversational Implicature with Controlled Context Variation22 May 2026
- 2026"Excuse me, may I say something..." CoLabScience, A Proactive AI Assistant for Biomedical Discovery and LLM-Expert Collaborations16 Apr 2026
- 2026Profiling learners' affective engagement: Emotion AI, intercultural pragmatics, and language learning20 Mar 2026
- 2025Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactions19 Feb 2025
- 2025GuideLLM: Exploring LLM-Guided Conversation with Applications in Autobiography Interviewing10 Feb 2025
- 2024Generative AI, Pragmatics, and Authenticity in Second Language Learning18 Oct 2024