Can LLMs Infer Conversational Agent Users' Personality Traits from Chat History?
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
Sensitive information, such as knowledge about an individual's personality, can be can be misused to influence behavior (e.g., via personalized messaging). To assess to what extent an individual's personality can be inferred from user interactions with LLM-based conversational agents (CAs), we analyze and quantify related privacy risks of using CAs. We collected actual ChatGPT logs from N=668 participants, containing 62,090 individual chats, and report statistics about the different types of shared data and use cases. We fine-tuned RoBERTa-base text classification models to infer personality traits from CA interactions. The findings show that these models achieve trait inference with accuracy (ternary classification) better than random in multiple cases. For example, for extraversion, accuracy improves by +44% relative to the baseline on interactions for relationships and personal reflection. This research highlights how interactions with CAs pose privacy risks and provides fine-grained insights into the level of risk associated with different types of interactions.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 4 of its 38 references that are in the map. First posted on arXiv 31 Mar 2026.
Builds on
4 of 38 references.
References · 4
- 2023Beyond Memorization: Violating Privacy Via Inference with Large Language Modelscited by 167
- 2020Dos and Don'ts of Machine Learning in Computer Securitycited by 98
- 2023Large Language Models Can Infer Psychological Dispositions of Social Media Userscited by 28
- 2016Phone-based Metric as a Predictor for Basic Personality Traitscited by 3
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2023"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents
- 2025Understanding Privacy Norms Around LLM-Based Chatbots: A Contextual Integrity Perspective
- 2025Can LLMs Infer Personality from Real World Conversations?
- 2023PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits
- 2024A Synthetic Dataset for Personal Attribute Inference
- 2026When Are LLM Inferences Acceptable? User Reactions and Control Preferences for Inferred Personal Information
- 2024BIG5-CHAT: Shaping LLM Personalities Through Training on Human-Grounded Data
- 2024LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models