Year: 2025
Type: conference-paper
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.
Authors: Shuhang Xu, Fangwei Zhong
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G6466790473
OpenAlex ID: W4412945665