Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
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.
Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 63 of its 146 references that are in the map. First posted on arXiv 18 Feb 2025.
Builds on
63 of 146 references.
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