From Persona to Personalization: A Survey on Role-Playing Language Agents
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
Recent advancements in large language models (LLMs) have significantly boosted the rise of Role-Playing Language Agents (RPLAs), i.e., specialized AI systems designed to simulate assigned personas. By harnessing multiple advanced abilities of LLMs, including in-context learning, instruction following, and social intelligence, RPLAs achieve a remarkable sense of human likeness and vivid role-playing performance. RPLAs can mimic a wide range of personas, ranging from historical figures and fictional characters to real-life individuals. Consequently, they have catalyzed numerous AI applications, such as emotional companions, interactive video games, personalized assistants and copilots, and digital clones. In this paper, we conduct a comprehensive survey of this field, illustrating the evolution and recent progress in RPLAs integrating with cutting-edge LLM technologies. We categorize personas into three types: 1) Demographic Persona, which leverages statistical stereotypes; 2) Character Persona, focused on well-established figures; and 3) Individualized Persona, customized through ongoing user interactions for personalized services. We begin by presenting a comprehensive overview of current methodologies for RPLAs, followed by the details for each persona type, covering corresponding data sourcing, agent construction, and evaluation. Afterward, we discuss the fundamental risks, existing limitations, and future prospects of RPLAs. Additionally, we provide a brief review of RPLAs in AI applications, which reflects practical user demands that shape and drive RPLA research. Through this work, we aim to establish a clear taxonomy of RPLA research and applications, and facilitate future research in this critical and ever-evolving field, and pave the way for a future where humans and RPLAs coexist in harmony.
Key facts. Cited by 212 papers in the map (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 214 of its 289 references that are in the map. First posted on arXiv 28 Apr 2024.
Builds on
214 of 289 references.
Closest ancestors · 8
Same direction, newest first
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- 2023Humanoid Agents: Platform for Simulating Human-like Generative Agents9 Oct
- 2023Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View3 Oct
- 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr
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Citing papers per year: 2024: 38; 2025: 99; 2026: 75.
212 papers.
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- 2026ANIMASK: What the Model Contributes to Role Play in Simulated Story Worlds15 Sep 2026
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