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        <title>CoMet: Metaphor-Driven Covert Communication for Multi-Agent Language Games</title>
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    <abstract>Metaphors are a crucial way for humans to express complex or subtle ideas by comparing one concept to another, often from a different domain. However, many large language models (LLMs) struggle to interpret and apply metaphors in multi-agent language games, hindering their ability to engage in covert communication and semantic evasion, which are crucial for strategic communication. To address this challenge, we introduce CoMet, a framework that enables LLM-based agents to engage in metaphor processing. CoMet combines a hypothesis-based metaphor reasoner with a metaphor generator that improves through self-reflection and knowledge integration. This enhances the agents’ ability to interpret and apply metaphors, improving the strategic and nuanced quality of their interactions. We evaluate CoMet on two multi-agent language games—Undercover and Adversarial Taboo—which emphasize “covert communication” and “semantic evasion”. Experimental results demonstrate that CoMet significantly enhances the agents’ ability to communicate strategically using metaphors.</abstract>
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%0 Conference Proceedings
%T CoMet: Metaphor-Driven Covert Communication for Multi-Agent Language Games
%A Xu, Shuhang
%A Zhong, Fangwei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F xu-zhong-2025-comet
%X Metaphors are a crucial way for humans to express complex or subtle ideas by comparing one concept to another, often from a different domain. However, many large language models (LLMs) struggle to interpret and apply metaphors in multi-agent language games, hindering their ability to engage in covert communication and semantic evasion, which are crucial for strategic communication. To address this challenge, we introduce CoMet, a framework that enables LLM-based agents to engage in metaphor processing. CoMet combines a hypothesis-based metaphor reasoner with a metaphor generator that improves through self-reflection and knowledge integration. This enhances the agents’ ability to interpret and apply metaphors, improving the strategic and nuanced quality of their interactions. We evaluate CoMet on two multi-agent language games—Undercover and Adversarial Taboo—which emphasize “covert communication” and “semantic evasion”. Experimental results demonstrate that CoMet significantly enhances the agents’ ability to communicate strategically using metaphors.
%R 10.18653/v1/2025.acl-long.389
%U https://aclanthology.org/2025.acl-long.389/
%U https://doi.org/10.18653/v1/2025.acl-long.389
%P 7892-7917

Markdown (Informal)

[CoMet: Metaphor-Driven Covert Communication for Multi-Agent Language Games](https://aclanthology.org/2025.acl-long.389/) (Xu & Zhong, ACL 2025)

• CoMet: Metaphor-Driven Covert Communication for Multi-Agent Language Games (Xu & Zhong, ACL 2025)

ACL

• Shuhang Xu and Fangwei Zhong. 2025. CoMet: Metaphor-Driven Covert Communication for Multi-Agent Language Games. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7892–7917, Vienna, Austria. Association for Computational Linguistics.