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）被视为能够高度模拟人类智能的系统。这一能力引起了社会科学界的关注，他们看到了利用LLMs替代人类实验参与者、从而降低研究成本与复杂性的潜力。本文提出了一套大型语言模型拟人化框架，包括从零构建虚拟角色人生故事的策略、模拟人类认知过程的多智能体认知机制，以及基于心理学指导的评估方法——从自我视角与观察者双重视角评估人类模拟效果。实验结果表明，我们构建的模拟物能够生成与其目标角色相一致的拟人化响应。该研究作为初步探索，在实际应用中展现出巨大潜力。所有代码与数据集将予以开源，以期激发进一步研究。