  Utilizing large language models (LLMs) to achieve role-playing has gained
great attention recently. The primary implementation methods include leveraging
refined prompts and fine-tuning on role-specific datasets. However, these
methods suffer from insufficient precision and limited flexibility
respectively. To achieve a balance between flexibility and precision, we
construct a Hierarchical Identity Role-Playing Framework (HIRPF) based on
identity theory, constructing complex characters using multiple identity
combinations. We develop an identity dialogue dataset for this framework and
propose an evaluation benchmark including scale evaluation and open situation
evaluation. Empirical results indicate the remarkable efficacy of our framework
in modeling identity-level role simulation, and reveal its potential for
application in social simulation.
