  The advent of Large Language Models (LLMs) has paved the way for complex
tasks such as role-playing, which enhances user interactions by enabling models
to imitate various characters. However, the closed-source nature of
state-of-the-art LLMs and their general-purpose training limit role-playing
optimization. In this paper, we introduce RoleLLM, a framework to benchmark,
elicit, and enhance role-playing abilities in LLMs. RoleLLM comprises four
stages: (1) Role Profile Construction for 100 roles; (2) Context-Based
Instruction Generation (Context-Instruct) for role-specific knowledge
extraction; (3) Role Prompting using GPT (RoleGPT) for speaking style
imitation; and (4) Role-Conditioned Instruction Tuning (RoCIT) for fine-tuning
open-source models along with role customization. By Context-Instruct and
RoleGPT, we create RoleBench, the first systematic and fine-grained
character-level benchmark dataset for role-playing with 168,093 samples.
Moreover, RoCIT on RoleBench yields RoleLLaMA (English) and RoleGLM (Chinese),
significantly enhancing role-playing abilities and even achieving comparable
results with RoleGPT (using GPT-4).
