A Survey on LLM-Driven Personality

ACL ARR 2025 May Submission5000 Authors

Abstract: With the growing use of large language models (LLMs) in social, educational, and assistive contexts, understanding and controlling their personality traits has become increasingly important. In this survey, we provide a comprehensive overview of personality modeling in LLMs, covering methods ranging from rule-based systems to prompt engineering, fine-tuning, agent and retrieval techniques, as well as approaches to multimodal setups. We examine both qualitative and quantitative evaluation protocols, and identify key challenges including subjectivity, context dependence, and limited multimodal integration. We conclude by outlining open questions and future directions for building consistent, expressive, and trustworthy persona-driven LLMs.

Paper Type: Long

Research Area: Dialogue and Interactive Systems

Research Area Keywords: conversational modeling, embodied agents, human-AI interaction/cooperation

Contribution Types: Surveys

Languages Studied: English

Submission Number: 5000