让大模型用隐喻实现隐秘沟通，提升多智能体博弈策略性。
CoMet: Metaphor-Driven Covert Communication for Multi-Agent Language Games
- 基于假设的隐喻推理与自反思生成机制，增强语言理解。
- 在暗语和禁忌词游戏中，隐喻沟通成功率显著提升。
- 适合研究隐性对话、博弈策略或认知模拟的学者。
隐喻是人类通过跨领域类比表达复杂或微妙思想的重要方式。然而，许多大语言模型在多智能体语言游戏中难以理解与运用隐喻，限制了其在隐秘通信与语义规避方面的能力，而这正是战略沟通的关键。为此，我们提出CoMet框架，使基于大语言模型的智能体能够进行隐喻处理。CoMet结合基于假设的隐喻推理器与通过自我反思和知识融合不断优化的隐喻生成器，显著提升了智能体对隐喻的理解与应用能力，增强了交互的战略性与细腻度。我们在两个强调隐秘通信与语义规避的多智能体语言游戏——Undercover和Adversarial Taboo上进行了评估。实验结果表明，CoMet显著提升了智能体使用隐喻进行战略性沟通的能力。
原文摘要 · Abstract (English)
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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