Character-LLM: A Trainable Agent for Role-Playing
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
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 textitmemorize their characters and experiences. Experimental results show interesting observations that help build future simulacra of humankind.
Key facts. Cited by 339 papers in the map (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 23 of its 106 references that are in the map. First posted on arXiv 16 Oct 2023.
Builds on
23 of 106 references.
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Other references · 22
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Citing papers per year: 2023: 10; 2024: 103; 2025: 118; 2026: 108.
339 papers.
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