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.

翻译：个性化大语言模型（LLMs）在智能教育、情感支持等诸多应用中受到广泛关注。现有研究多集中于基于用户画像（如年龄、技能、经历等）控制角色设定，而基于心理学理论、具有隐性表达与行为特征的人格特质尚未得到充分建模，这限制了其在心理咨询代理等专业领域的潜在应用。本文提出一种基于混合专家（MoE）的个性化大语言模型P-Tailor，用于建模大五人格特质。具体而言，我们通过训练专用的LoRA专家模块来表征开放性、尽责性、外向性、宜人性与神经质等不同特质，并引入人格特化损失函数，促使各专家模块专注于特定人格特质，从而提升模型参数利用效率。针对现有数据集的不足，我们构建了高质量的人格塑造数据集（PCD），用于学习和培养模型在不同主题下展现差异化人格特质的能力。大量实验验证了P-Tailor在细粒度调控大语言模型人格特质方面具有优异性能与有效性。