P -React: Synthesizing Topic-Adaptive Reactions of Personality Traits via Mixture of Specialized L o RA Experts (2025)

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.

## introduction recent advancements in large language models (llms) have showcased their ability to follow instructions @xcite , reason step-bystep @xcite , and simulate human-like behaviors @xcite . these advancements have also fueled the demand for personalized ai experiences, leading to the emergence of many tailored llms, such as role-playing games @xcite and virtual role-playing agents @xcite . however, existing works on personalized llms mainly focus on simulating character profiles with surface-level attributes, such as name, skill, experience, and so on (see figure 1(b) ) @xcite , 2023; @xcite @xcite @xcite , while ignoring the underlying personality traits that truly shape behaviors and decision-making @xcite @xcite . this limitation becomes particularly evident in applications requiring consistent and realistic personality traits, such as psychological counseling and digital tutoring. in addition, the scarcity of large-scale, high-quality dialogue datasets with personality annotations has also hampered the exploration of personality trait simulation. for our theoretical foundation, we adopt the big five personality traits @xcite , which has emerged as the most widely used personality descriptor in psychology due to its relia-bility and cross-cultural validity @xcite . the model delineates personality through five dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism, as shown in figure 1 @xcite . each dimension represents a unique aspect of personality, highlighting the need for a modeling approach capable of capturing the unique reaction of each trait. to overcome the data scarcity challenge, we construct ocean-chat, a high-quality humanvalidated dataset encompassing multi-turn dialogues exhibiting reactions of big five personality traits across diverse conversational topics. to tackle the challenge of authentic personality simulation in language models, we introduce p-react, a personality-customizable model built on the lo-ramoe architecture @xcite . our analysis reveals that loramoe's experts learn personality dimensions in an undifferentiated manner, with each expert attempting to model all traits equally (see section 6.4). this contradicts the inherent nature of the big five personality traits, where reactions to each dimension are distinct. to address this limitation, we develop the personality specialization loss (psl), which guides each expert to focus on modeling reactions to specific personality traits. this paper makes the following contributions: the objective of this work is to enable large language models to simulate various personality traits. each personality trait (detailed in ## ocean-chat dataset we present ocean-chat, a high-quality, humanverified dialogue dataset designed to shape the personality of large language models (llms). our work is grounded in the big five personality traits @xcite , also known as the five-factor model (ffm), which is widely used in psychology. definitions of the big five personality dimensions are detailed in the appendix d. ocean-chat provides a solid foundation for modeling distinct personality trait reactions in llms through a rich collection of dialogues across diverse topics, effectively capturing personality-driven behaviors and interaction patterns. ## p-react in this section, we provide a comprehensive description of p-react. we first introduce its structure (from section 4.1 to section 4.2), then detail the integration of personality specialization loss (section 4.3), and finally demonstrate the training and inference of p-react (section 4.4). ## experimental analysis our analysis aims to address the following research questions (rqs): ## conclusions in this paper, we introduce p-react, a novel personality-customizable model that captures big five personality traits through a mixture of specialized lora experts with personality specialization loss (psl). we curate ocean-chat, a humanvalidated dataset of dialogues showcasing authentic personality reactions across diverse topics. extensive experiments demonstrate that p-react significantly outperforms existing approaches in personality trait simulation while maintaining high response quality. visualization analysis reveals that psl enables experts to effectively specialize in specific traits, aligning with psychological theory. in future work, we plan to extend p-react to model more nuanced personality theories and explore its applications in mental health support and personalized education.