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About this paper
Bingsheng Yao, Bo Sun, Yuanzhe Dong, Yuxuan Lu, Dakuo Wang
The Dynamic Persona Refinement Framework (DPRF) iteratively refines persona profiles for LLM role-playing agents to enhance their behavioral alignment with human ground truth across diverse cognitive tasks. It achieves consistent performance gains by systematically identifying and integrating cognitive divergences, showing that optimal behavior analysis strategies are task-dependent.
Key insights
- More capable underlying LLMs lead to larger and more consistent performance gains within DPRF, highlighting the importance of the base model's analytical power for effective refinement.
- The optimal strategy for behavior analysis is task-dependent: free-form analysis is more effective for emotion-centric tasks, while theory-grounded (ToM) analysis is superior for cognitively complex tasks.
- Providing the Behavior Analysis Agent with the current persona description is crucial, as it enables the agent to assess behaviors against the intended identity, leading to more targeted divergence identification.
Summary by alphaXiv.
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