Role-Playing Agents Driven by Large Language Models: Current Status, Challenges, and Future Trends

TL;DR

This paper reviews the development, key technologies, evaluation methods, and future directions of role-playing agents driven by large language models, highlighting technological evolution, challenges, and potential research trends.

Contribution

It provides a comprehensive systematic review of RPLAs, covering technological evolution, data construction, evaluation frameworks, and future research directions in the field.

Findings

Technological evolution from rule-based to cognitive simulation stages.

Analysis of data sources, copyright issues, and annotation challenges.

Evaluation frameworks include human judgment, reward models, and LLM-based scoring.

Abstract

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.