Large language models (LLMs) are recognized as systems that closely mimic aspects of human intelligence. This capability has attracted attention from the social science community, who see the potential in leveraging LLMs to replace human participants in experiments, thereby reducing research costs and complexity. In this paper, we introduce a framework for large language models personification, including a strategy for constructing virtual characters' life stories from the ground up, a Multi-Agent Cognitive Mechanism capable of simulating human cognitive processes, and a psychology-guided evaluation method to assess human simulations from both self and observational perspectives. Experimental results demonstrate that our constructed simulacra can produce personified responses that align with their target characters. Our work is a preliminary exploration which offers great potential in practical applications. All the code and datasets will be released, with the hope of inspiring further investigations.

翻译：大型语言模型（LLMs）被认为能紧密模仿人类智能的诸多方面。这一能力引起了社会科学界的关注，他们看到利用LLMs替代人类参与者进行实验的潜力，从而降低研究成本和复杂性。本文提出了一种大型语言模型拟人化框架，包括从头构建虚拟角色人生故事的策略、能够模拟人类认知过程的多智能体认知机制，以及一种心理学引导的评估方法，可从自我和观察者双重视角评估人类模拟效果。实验结果表明，我们构建的仿真体能产生与其目标角色一致的拟人化响应。本工作是一项初步探索，但在实际应用中具有巨大潜力。所有代码和数据集将公开发布，以期激发进一步的研究。