AI model inference training

RoleLLM is a role-playing framework for building and evaluating the role-playing capabilities of large language models. It consists of four stages: role profile construction, context-based command generation, role prompting using GPT, and role-based command adjustment. Through Context-Instruct and RoleGPT, we create RoleBench, a systematic and fine-grained role-level benchmark dataset containing 168,093 samples. In addition, RoCIT produces RoleLLaMA (English) and RoleGLM (Chinese) on RoleBench, which significantly improves role-playing capabilities and even achieves comparable results with RoleGPT using GPT-4.