P-React: Synthesizing Topic-Adaptive Reactions of Personality Traits via Mixture of Specialized LoRA Experts

• URL: http://arxiv.org/abs/2406.12548v3
• Date: Thu, 24 Jul 2025 16:54:18 GMT
• Title: P-React: Synthesizing Topic-Adaptive Reactions of Personality Traits via Mixture of Specialized LoRA Experts
• Authors: Yuhao Dan, Jie Zhou, Qin Chen, Junfeng Tian, Liang He,
• Abstract summary: We propose P-React, a mixture of experts (MoE)-based personalized large language models.<n> Particularly, we integrate a Personality Loss (PSL) to better capture individual trait expressions.<n>To facilitate research in this field, we curate OCEAN-Chat, a high-quality, human-verified dataset.
• Score: 34.374681921626205
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Personalized large language models (LLMs) have attracted great attention in many applications, such as emotional support and role-playing. However, existing works primarily focus on modeling explicit character profiles, while ignoring the underlying personality traits that truly shape behaviors and decision-making, hampering the development of more anthropomorphic and psychologically-grounded AI systems. In this paper, we explore the modeling of Big Five personality traits, which is the most widely used trait theory in psychology, and propose P-React, a mixture of experts (MoE)-based personalized LLM. Particularly, we integrate a Personality Specialization Loss (PSL) to better capture individual trait expressions, providing a more nuanced and psychologically grounded personality simulacrum. To facilitate research in this field, we curate OCEAN-Chat, a high-quality, human-verified dataset designed to train LLMs in expressing personality traits across diverse topics. Extensive experiments demonstrate the effectiveness of P-React in maintaining consistent and real personality.

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