由大型语言模型驱动的角色扮演代理：现状、挑战与未来趋势
Role-Playing Agents Driven by Large Language Models: Current Status, Challenges, and Future Trends
AI总结：
本文系统回顾了由大型语言模型驱动的角色扮演代理的现状、挑战及未来趋势，探讨了关键技术路径与评估方法。
摘要：
近年来，随着大型语言模型（LLMs）的快速进步，角色扮演语言代理（RPLAs）在自然语言处理（NLP）和人机交互的交汇处成为研究焦点。本文系统地回顾了RPLAs的当前发展和关键技术，阐述了从早期基于规则的模板范式，经过语言风格模仿阶段，到以人格建模和记忆机制为核心的认知模拟阶段的技术演变。它总结了支持高质量角色扮演的关键技术路径，包括心理尺度驱动的角色建模、记忆增强的提示机制，以及基于动机和情境的行为决策控制。在数据层面，本文进一步分析了构建角色特定语料库的方法和挑战，重点在于数据来源、版权限制和结构化标注过程。在评估方面，它汇总了涵盖角色知识、人格忠实度、价值对齐和交互幻觉的多维评估框架和基准数据集，同时评论了人类评估、奖励模型和基于LLM的评分等方法的优缺点。最后，本文概述了角色扮演代理的未来发展方向，包括人格进化建模、多代理协作叙事、多模态沉浸交互以及与认知神经科学的整合，旨在为后续研究提供系统性的视角和方法论见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
