Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents

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.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.findings-acl.938
Citations
1
Primary Topic
Robotic Process Automation Applications
Type
article
Field-Weighted Citation Impact
1.24
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article

Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents

Toby Jia-Jun Li, Wenyue Hua, De Yun Wang, Chaoran Chen et al.
1 citations
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article

Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents

Toby Jia-Jun Li, Wenyue Hua, De Yun Wang, Chaoran Chen, Bingsheng Yao, Ruishi Zou, Weimin Lyu
article en
1 citations

Abstract

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.

Quality Education
Openalex Percentile: Top 17%
Robotic Process Automation Applications
1.24
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