Role-Playing Evaluation for Large Language Models
This work addresses the problem of resource-intensive and biased evaluations for LLM role-playing, offering a new benchmark for researchers and developers, though it appears incremental as it builds on existing evaluation methods.
The authors tackled the challenge of evaluating large language models' role-playing abilities by introducing Role-Playing Eval (RPEval), a benchmark that assesses capabilities across emotional understanding, decision-making, moral alignment, and in-character consistency, with baseline evaluations provided.
Large Language Models (LLMs) demonstrate a notable capacity for adopting personas and engaging in role-playing. However, evaluating this ability presents significant challenges, as human assessments are resource-intensive and automated evaluations can be biased. To address this, we introduce Role-Playing Eval (RPEval), a novel benchmark designed to assess LLM role-playing capabilities across four key dimensions: emotional understanding, decision-making, moral alignment, and in-character consistency. This article details the construction of RPEval and presents baseline evaluations. Our code and dataset are available at https://github.com/yelboudouri/RPEval