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About this paper
Ye Wang, Jiaxing Chen, Hongjiang Xiao
A comprehensive overview of Role-Playing Language Agents (RPLAs) driven by large language models (LLMs) is presented, detailing their current development, core technologies, data challenges, and evaluation methods. The work outlines the technological evolution of RPLAs from early rule-based systems to advanced cognitive simulations, identifying critical research gaps and future directions.
Key insights
- RPLAs are evolving from mere linguistic imitators to cognitive agents, integrating complex personality, memory, and motivation-driven decision-making.
- High-quality RPLA development relies on interdisciplinary integration, particularly psychological theories for personality modeling and advanced memory architectures for long-term consistency.
- Despite advances in benchmark systems, current evaluation methods still struggle to quantitatively assess nuanced aspects like personality fidelity, behavioral logic, and emotional consistency over extended interactions.
Summary by alphaXiv.
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