CoMet: Metaphor-Driven Covert Communication for Multi-Agent Language Games

• URL: http://arxiv.org/abs/2505.18218v1
• Date: Fri, 23 May 2025 08:23:54 GMT
• Title: CoMet: Metaphor-Driven Covert Communication for Multi-Agent Language Games
• Authors: Shuhang Xu, Fangwei Zhong,
• Abstract summary: CoMet is a framework that enables large language models (LLMs) to interpret and apply metaphors in language games.<n>We evaluate CoMet on two multi-agent language games - Undercover and Adrial Taboo.
• Score: 9.574135232284657
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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