Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents
It addresses the problem of inconsistent evaluation methods for LLM-based RPAs, which is incremental as it synthesizes existing literature into a practical guideline.
This paper tackles the challenge of evaluating Role-Playing Agents (RPAs) by proposing an evidence-based design guideline derived from a systematic review of 1,676 papers, identifying key attributes and metrics to improve evaluation consistency.
Role-Playing Agent (RPA) is an increasingly popular type of LLM Agent that simulates human-like behaviors in a variety of tasks. However, evaluating RPAs is challenging due to diverse task requirements and agent designs. This paper proposes an evidence-based, actionable, and generalizable evaluation design guideline for LLM-based RPA by systematically reviewing 1,676 papers published between Jan. 2021 and Dec. 2024. Our analysis identifies six agent attributes, seven task attributes, and seven evaluation metrics from existing literature. Based on these findings, we present an RPA evaluation design guideline to help researchers develop more systematic and consistent evaluation methods.