Role-playing is important for Large Language Models (LLMs) to follow diverse instructions while maintaining role identity and the role's pre-defined ability limits. Existing role-playing datasets mostly contribute to controlling role style and knowledge boundaries, but overlook role-playing in instruction-following scenarios. We introduce a fine-grained role-playing and instruction-following composite benchmark, named RoleMRC, including: (1) Multi-turn dialogues between ideal roles and humans, including free chats or discussions upon given passages; (2) Role-playing machine reading comprehension, involving response, refusal, and attempts according to passage answerability and role ability; (3) More complex scenarios with nested, multi-turn and prioritized instructions. The final RoleMRC features a 10.2k role profile meta-pool, 37.9k well-synthesized role-playing instructions, and 1.4k testing samples. We develop a pipeline to quantitatively evaluate the fine-grained role-playing and instruction-following capabilities of several mainstream LLMs, as well as models that are fine-tuned on our data. Moreover, cross-evaluation on external role-playing datasets confirms that models fine-tuned on RoleMRC enhances instruction-following without compromising general role-playing and reasoning capabilities. We also probe the neural-level activation maps of different capabilities over post-tuned LLMs.
- check our paper, data and local post-tuned models.
- check training , evaluation, and interpretation codes, respectively.
This code is built upon the TRL repository.
@article{LUandLI2025RoleMRC,
title={RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following},
author={Lu, Junru and Li, Jiazheng and Shen, Guodong and Gui, Lin and An, Siyu and He, Yulan and Yin, Di and Sun, Xing},
journal={arXiv preprint arXiv:2502.11387},
year={2025}
}
