CharacterEval: A Chinese Benchmark for Role-Playing Conversational Agent Evaluation
Quan Tu, Shilong Fan, Zihang Tian, Tianhao Shen, Shuo Shang, Xin Rui Gao, Rui Yan
Abstract
Recently, the advent of large language models (LLMs) has revolutionized generative agents.Among them, Role-Playing Conversational Agents (RPCAs) attract considerable attention due to their ability to emotionally engage users.However, the absence of a comprehensive benchmark impedes progress in this field.To bridge this gap, we introduce Char-acterEval, a Chinese benchmark for comprehensive RPCA assessment, complemented by a tailored high-quality dataset.The dataset comprises 1,785 multi-turn role-playing dialogues, encompassing 11,376 examples and featuring 77 characters derived from Chinese novels and scripts.It was carefully constructed, beginning with initial dialogue extraction via GPT-4, followed by rigorous human-led quality control, and enhanced with in-depth character profiles sourced from Baidu Baike.Charac-terEval employs a multifaceted evaluation approach, encompassing thirteen targeted metrics on four dimensions.To facilitate the convenient evaluation for these subjective metrics in CharacterEval, we further developed Charac-terRM, a role-playing reward model based on human annotations, which has a higher correlation with human judgment compared to GPT-4.Comprehensive experiments on Char-acterEval demonstrate that Chinese LLMs exhibit more promising capabilities than GPT-4 in Chinese role-playing conversation.Source code, data source, and reward model will be publicly accessible at https://github.com/ morecry/CharacterEval.