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

ACL ARR 2025 February Submission1232 Authors

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.

Paper Type: Long

Research Area: Dialogue and Interactive Systems

Research Area Keywords: LLM Agent; Metapohor; Covert Communication; Language Game;

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models

Languages Studied: English

Submission Number: 1232