Fairness Testing of Large Language Models in Role-Playing

Abstract

Large Language Models (LLMs) have become foundational in modern language-driven software applications, profoundly influencing daily life. A critical technique in leveraging their potential is role-playing, where LLMs simulate diverse roles to enhance their real-world utility. However, while research has highlighted the presence of social biases in LLM outputs, it remains unclear whether and to what extent these biases emerge during role-playing scenarios. In this paper, we conduct an empirical study on fairness testing of LLMs in role-playing scenarios. To enable this testing, we use LLMs to generate 550 social roles spanning a comprehensive set of 11 demographic attributes, producing 33,000 role-specific questions that target various forms of bias. These questions, covering Yes/No, multiple-choice, and open-ended formats, are designed to prompt LLMs to adopt specific roles and respond accordingly. We employ a combination of rule-based and LLM-based strategies to identify biased responses, rigorously validated through human evaluation. Using the generated questions as the test cases, we conduct extensive evaluations of 10 advanced LLMs. The evaluation reveal 107,580 biased responses across the studied LLMs, with individual models yielding between 7,579 and 16,963 biased responses, underscoring the prevalence of bias in role-playing contexts. To support future research, we have publicly released the dataset, along with all scripts and experimental results.