Character-LLM: A Trainable Agent for Role-Playing

By Yunfan Shao, Linyang Li, Junqi Dai, Xipeng Qiu. Conference on Empirical Methods in Natural Language Processing, 2023

475 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/6628f9ee35e36cdfdcac8a46cef4dba8d529a83b

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In Artificial Intelligence

Citations in the corpus

PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits (2023, background): “…human-like behavior in reasoning (Dasgupta et al., 2022; Webb et al., 2023; Binz and Schulz, 2023; Aher et al., 2023; Wong et al., 2023), role-playing (Wang et al., 2023b; Shao et al., 2023; Wang et al., 2023a), and social science experiments (Horton, 2023; Park et al., 2023; Ziems et al., 2023).”
SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents (2023, background): “There are works that focus on investigating the fidelity of LLMs in keeping the designated persona and experiences of the characters (Shao et al., 2023; Jiang et al., 2023).”
A Survey on the Memory Mechanism of Large Language Model-based Agents (2024, unknown): “Unlike task-oriented agents where greater capability is typically preferred, the proficiency of a humanoid agent should closely mimic that of humans.”