Identity-Driven Hierarchical Role-Playing Agents
This work addresses the challenge of creating more precise and flexible role-playing agents for applications in social simulation, representing an incremental improvement over existing methods.
The paper tackles the problem of insufficient precision and limited flexibility in role-playing with large language models by proposing a Hierarchical Identity Role-Playing Framework (HIRPF) based on identity theory, achieving effective identity-level role simulation with potential applications in social simulation.
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.