DMT-RoleBench: A Dynamic Multi-Turn Dialogue Based Benchmark for Role-Playing Evaluation of Large Language Model and Agent

Abstract

Recent years have witnessed a profound evolution in the abilities of Large Language Model, which has significantly boosted the proliferation of role-playing agents and platforms. Nonetheless, there is a conspicuous absence of systematic and comprehensive evaluations of role-playing abilities which are truly aligned with users' interaction scenarios in real-world. To address this gap, we have devised DMT-RoleBench, a benchmark designed to evaluate the role-playing abilities of large language models and agents based on dynamic multi-turn dialogues. Compared with existed role-playing benchmarks, DMT-RoleBench boasts several principal advantages: (1) It contains a more diverse role types and system prompts of different formats. (2) We propose an innovative evaluation paradigm to assess role-playing abilities based on dynamically generating multi-turn dialogues constrained by specific evaluation intents and topics, which is well aligned with users' interaction scenarios in real-world. (3) We define a three-tiered metric system and provide DMT-RM, which is a reward model aligned with human annotations, to annotate the dialogues. And we propose DMT-Score to calculate the final scores based on the annotated dialogues. Our experiments and analysis of leading models equipped with role-playing abilities have demonstrated the effectiveness of DMT-RoleBench.