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    <abstract>While Large Language Models (LLMs) have demonstrated impressive accomplishments in both reasoning and planning, their abilities in multi-agent collaborations remains largely unexplored. This study evaluates LLM-based agents in a multi-agent cooperative text game with Theory of Mind (ToM) inference tasks, comparing their performance with Multi-Agent Reinforcement Learning (MARL) and planning-based baselines. We observed evidence of emergent collaborative behaviors and high-order Theory of Mind capabilities among LLM-based agents. Our results reveal limitations in LLM-based agents’ planning optimization due to systematic failures in managing long-horizon contexts and hallucination about the task state. We explore the use of explicit belief state representations to mitigate these issues, finding that it enhances task performance and the accuracy of ToM inferences for LLM-based agents.</abstract>
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%0 Conference Proceedings
%T Theory of Mind for Multi-Agent Collaboration via Large Language Models
%A Li, Huao
%A Chong, Yu
%A Stepputtis, Simon
%A Campbell, Joseph
%A Hughes, Dana
%A Lewis, Charles
%A Sycara, Katia
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-theory
%X While Large Language Models (LLMs) have demonstrated impressive accomplishments in both reasoning and planning, their abilities in multi-agent collaborations remains largely unexplored. This study evaluates LLM-based agents in a multi-agent cooperative text game with Theory of Mind (ToM) inference tasks, comparing their performance with Multi-Agent Reinforcement Learning (MARL) and planning-based baselines. We observed evidence of emergent collaborative behaviors and high-order Theory of Mind capabilities among LLM-based agents. Our results reveal limitations in LLM-based agents’ planning optimization due to systematic failures in managing long-horizon contexts and hallucination about the task state. We explore the use of explicit belief state representations to mitigate these issues, finding that it enhances task performance and the accuracy of ToM inferences for LLM-based agents.
%R 10.18653/v1/2023.emnlp-main.13
%U https://aclanthology.org/2023.emnlp-main.13
%U https://doi.org/10.18653/v1/2023.emnlp-main.13
%P 180-192

Markdown (Informal)

[Theory of Mind for Multi-Agent Collaboration via Large Language Models](https://aclanthology.org/2023.emnlp-main.13) (Li et al., EMNLP 2023)

• Theory of Mind for Multi-Agent Collaboration via Large Language Models (Li et al., EMNLP 2023)

ACL

• Huao Li, Yu Chong, Simon Stepputtis, Joseph Campbell, Dana Hughes, Charles Lewis, and Katia Sycara. 2023. Theory of Mind for Multi-Agent Collaboration via Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 180–192, Singapore. Association for Computational Linguistics.