  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.
