In recent years, with the rapid advancement of large language models (LLMs), role-playing language agents (RPLAs) have emerged as a prominent research focus at the intersection of natural language processing (NLP) and human-computer interaction. This paper systematically reviews the current development and key technologies of RPLAs, delineating the technological evolution from early rule-based template paradigms, through the language style imitation stage, to the cognitive simulation stage centered on personality modeling and memory mechanisms. It summarizes the critical technical pathways supporting high-quality role-playing, including psychological scale-driven character modeling, memory-augmented prompting mechanisms, and motivation-situation-based behavioral decision control. At the data level, the paper further analyzes the methods and challenges of constructing role-specific corpora, focusing on data sources, copyright constraints, and structured annotation processes. In terms of evaluation, it collates multi-dimensional assessment frameworks and benchmark datasets covering role knowledge, personality fidelity, value alignment, and interactive hallucination, while commenting on the advantages and disadvantages of methods such as human evaluation, reward models, and LLM-based scoring. Finally, the paper outlines future development directions of role-playing agents, including personality evolution modeling, multi-agent collaborative narrative, multimodal immersive interaction, and integration with cognitive neuroscience, aiming to provide a systematic perspective and methodological insights for subsequent research.

翻译：近年来，随着大语言模型（LLMs）的快速发展，角色扮演语言智能体（RPLAs）已成为自然语言处理（NLP）与人机交互交叉领域的重要研究方向。本文系统梳理了RPLAs的发展现状与关键技术，厘清了从早期基于规则的模板范式，到语言风格模仿阶段，再到以人格建模与记忆机制为核心的认知模拟阶段的技术演进脉络。总结了支撑高质量角色扮演的关键技术路径，包括心理量表驱动的人物建模、记忆增强的提示机制以及基于动机-情境的行为决策控制。在数据层面，本文进一步分析了角色专属语料库的构建方法与挑战，重点关注数据来源、版权约束与结构化标注流程。在评估方面，整理了覆盖角色知识、人格保真度、价值对齐与交互幻觉等多维度的评估框架与基准数据集，并对人工评估、奖励模型、基于LLM的评分等方法进行了优劣评述。最后，本文展望了角色扮演智能体的未来发展方向，包括人格演化建模、多智能体协同叙事、多模态沉浸式交互以及与认知神经科学的结合，旨在为后续研究提供系统的视角与方法论启示。