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.

本文提出基于专家混合（MoE）的个性化大型语言模型P-tailor来建模五大人格特征，并将其与个性特化损失相结合，以促进专家在不同人格特征上的专业化，从而提高模型参数利用效率。作者还构建了高质量的人格塑造数据集（PCD），在各个主题下学习和展示不同的人格特征。通过广泛的实验验证了P-tailor在对LLMs的细粒度人格特征操作方面的出色性能和有效性。

P-Tailor: 通过专业 LoRA 专家的混合来定制语言模型的个性特质