Challenge: Existing methods for analyzing and analyzing large language models (LLMs) lack of emotion and fine-grained role awareness limits the model’s ability to provide personalized and diverse interactions further.
Approach: They propose an annotation-free framework to align LLMs’ behavior during role-playing, enhancing the model’s role consistency.
Outcome: The proposed framework outperforms vanilla LLMs under automatic evaluation methods and human expert evaluation.

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