Large language models (LLMs) can be used to serve as agents to simulate human behaviors, given the powerful ability to understand human instructions and provide high-quality generated texts. Such ability stimulates us to wonder whether LLMs can simulate a person in a higher form than simple human behaviors. Therefore, we aim to train an agent with the profile, experience, and emotional states of a specific person instead of using limited prompts to instruct ChatGPT API. In this work, we introduce Character-LLM that teach LLMs to act as specific people such as Beethoven, Queen Cleopatra, Julius Caesar, etc. Our method focuses on editing profiles as experiences of a certain character and training models to be personal simulacra with these experiences. To assess the effectiveness of our approach, we build a test playground that interviews trained agents and evaluates whether the agents \textit{memorize} their characters and experiences. Experimental results show interesting observations that help build future simulacra of humankind.

翻译：大型语言模型（LLMs）凭借理解人类指令和生成高质量文本的强大能力，可被用作模拟人类行为的智能体。这种能力促使我们思考：LLMs是否能以超越简单人类行为的高级形式模拟特定人物？因此，本文旨在基于特定人物的档案、经历和情感状态训练智能体，而非使用有限提示指令指导ChatGPT API。本研究提出Character-LLM，使LLMs能够扮演贝多芬、克利奥帕特拉女王、凯撒等具体人物。我们的方法侧重于将特定角色的经历编辑为档案，并通过这些经历将模型训练为个人模拟体。为评估该方法的有效性，我们构建了一个测试场域，对训练后的智能体进行访谈，并评估其是否能够“记忆”角色身份及经历。实验结果表明，这些有趣的发现将有助于构建未来的人类模拟体。