  Personalized large language models (LLMs) have attracted great attention in
many applications, such as intelligent education and emotional support. Most
work focuses on controlling the character settings based on the profile (e.g.,
age, skill, experience, and so on). Conversely, the psychological theory-based
personality traits with implicit expression and behavior are not well modeled,
limiting their potential application in more specialized fields such as the
psychological counseling agents. In this paper, we propose a mixture of experts
(MoE)-based personalized LLMs, named P-tailor, to model the Big Five
Personality Traits. Particularly, we learn specialized LoRA experts to
represent various traits, such as openness, conscientiousness, extraversion,
agreeableness and neuroticism. Then, we integrate P-Tailor with a personality
specialization loss, promoting experts to specialize in distinct personality
traits, thereby enhancing the efficiency of model parameter utilization. Due to
the lack of datasets, we also curate a high-quality personality crafting
dataset (PCD) to learn and develop the ability to exhibit different personality
traits across various topics. We conduct extensive experiments to verify the
great performance and effectiveness of P-Tailor in manipulation of the
fine-grained personality traits of LLMs.
