Enhancing Persona Consistency for LLMs' Role-Playing using Persona-Aware Contrastive Learning

• URL: http://arxiv.org/abs/2503.17662v2
• Date: Tue, 25 Mar 2025 14:43:35 GMT
• Title: Enhancing Persona Consistency for LLMs' Role-Playing using Persona-Aware Contrastive Learning
• Authors: Ke Ji, Yixin Lian, Linxu Li, Jingsheng Gao, Weiyuan Li, Bin Dai,
• Abstract summary: We propose a novel framework named textbfunderlinePersona-Aware textbfunderlineContrastive textbfunderlineLearning (PCL) to align model role-playing behavior.<n>We show that PCL significantly outperform vanilla LLMs under automatic evaluation methods and human expert evaluation.
• Score: 7.836439251883518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, large language models (LLMs) have achieved breakthrough progress in many dialogue generation tasks. However, their lack of emotion and fine-grained role awareness limits the model's ability to provide personalized and diverse interactions further. Current methods face high costs in collecting high-quality annotated data for scenarios such as role-playing, and traditional human alignment methods are difficult to deploy due to the inherent diversity of model behavior in role-playing scenarios. Inspired by the alignment of models for safety behaviors through RLHF (Reinforcement Learning from Human Feedback), in this paper, we revisit model role-playing behavior from the perspective of persona alignment and propose a novel annotation-free framework named \textbf{\underline{P}}ersona-Aware \textbf{\underline{C}}ontrastive \textbf{\underline{L}}earning (PCL) to align LLMs' behavior during role-playing, enhancing the model's role consistency. Specifically, we first design a role chain method to encourage the model to self-question based on the role characteristics and dialogue context to adjust personality consistency. Then, we further enhance the model's role-playing strategy through iterative contrastive learning between the use of role characteristics and not. Experiments on both black-box and white-box LLMs show that LLMs equipped with PCL significantly outperform vanilla LLMs under automatic evaluation methods (CharEval \& GPT-4) and human expert evaluation.

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