Large language models exhibit aspects of human-level intelligence that catalyze their application as human-like agents in domains such as social simulations, human-machine interactions, and collaborative multi-agent systems. However, the absence of distinct personalities, such as displaying ingratiating behaviors, inconsistent opinions, and uniform response patterns, diminish LLMs utility in practical applications. Addressing this, the development of personality traits in LLMs emerges as a crucial area of research to unlock their latent potential. Existing methods to personify LLMs generally involve strategies like employing stylized training data for instruction tuning or using prompt engineering to simulate different personalities. These methods only capture superficial linguistic styles instead of the core of personalities and are therefore not stable. In this study, we propose PersLLM, integrating psychology-grounded principles of personality: social practice, consistency, and dynamic development, into a comprehensive training methodology. We incorporate personality traits directly into the model parameters, enhancing the model's resistance to induction, promoting consistency, and supporting the dynamic evolution of personality. Single-agent evaluation validates our method's superiority, as it produces responses more aligned with reference personalities compared to other approaches. Case studies for multi-agent communication highlight its benefits in enhancing opinion consistency within individual agents and fostering collaborative creativity among multiple agents in dialogue contexts, potentially benefiting human simulation and multi-agent cooperation. Additionally, human-agent interaction evaluations indicate that our personified models significantly enhance interactive experiences, underscoring the practical implications of our research.

翻译：大语言模型展现出类人智能的多个维度，这推动了其作为类人智能体在社会模拟、人机交互以及协作多智能体系统等领域的应用。然而，由于缺乏鲜明的个性特征（例如表现出迎合行为、观点不一致以及统一的应答模式），大语言模型在实际应用中的效用受到限制。针对这一问题，在大语言模型中发展个性特质已成为释放其潜在能力的关键研究方向。现有的拟人化方法通常涉及采用风格化训练数据进行指令微调，或使用提示工程来模拟不同个性。这些方法仅捕捉了表面的语言风格而非个性核心，因此并不稳定。在本研究中，我们提出PersLLM，将基于心理学的人格原则——社会实践性、一致性与动态发展性——整合为一个综合的训练方法论。我们将人格特质直接融入模型参数，增强了模型对诱导的抵抗力，促进了一致性，并支持个性的动态演化。单智能体评估验证了我们方法的优越性，与其他方法相比，其生成的回答更符合参考人格特征。多智能体通信的案例研究凸显了该方法在增强单个智能体内观点一致性、促进多智能体在对话情境中协作创造力方面的优势，这可能有益于人类行为模拟与多智能体协作。此外，人机交互评估表明，我们的拟人化模型显著提升了交互体验，突显了本研究的实际应用价值。