Character-LLM: A Trainable Agent for Role-Playing

• URL: http://arxiv.org/abs/2310.10158v2
• Date: Thu, 14 Dec 2023 11:49:17 GMT
• Title: Character-LLM: A Trainable Agent for Role-Playing
• Authors: Yunfan Shao, Linyang Li, Junqi Dai, Xipeng Qiu
• Abstract summary: Large language models (LLMs) can be used to serve as agents to simulate human behaviors. We introduce Character-LLM that teach LLMs to act as specific people such as Beethoven, Queen Cleopatra, Julius Caesar, etc.
• Score: 67.35139167985008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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