Memory-Driven Role-Playing: Evaluation and Enhancement of Persona Knowledge Utilization in LLMs
A core challenge for faithful LLM role-playing is sustaining consistent characterization throughout long, open-ended dialogues, as models frequently fail to recall and accurately apply their designated persona knowledge without explicit cues. To tackle this, we propose the Memory-Driven Role-Playing paradigm. Inspired by Stanislavski's "emotional memory" acting theory, this paradigm frames persona knowledge as the LLM's internal memory store, requiring retrieval and application based solely on dialogue context, thereby providing a rigorous test of depth and autonomous use of knowledge. Centered on this paradigm, we contribute: (1) MREval, a fine-grained evaluation framework assessing four...
| Authors | Kai Wang, Haoyang You, Yang Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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