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Large Language Models (LLMs) have recently made significant strides incomplex reasoning tasks through the Chain-of-Thought technique. Despite thisprogress, their reasoning is often constrained by their intrinsicunderstanding, lacking external insights. To address this, we proposeExchange-of-Thought (EoT), a novel framework that enables cross-modelcommunication during problem-solving. Drawing inspiration from networktopology, EoT integrates four unique communication paradigms: Memory, Report,Relay, and Debate. This paper delves into the communication dynamics and volumeassociated with each paradigm. To counterbalance the risks of incorrectreasoning chains, we implement a robust confidence evaluation mechanism withinthese communications. Our experiments across diverse complex reasoning tasksdemonstrate that EoT significantly surpasses established baselines,underscoring the value of external insights in enhancing LLM performance.Furthermore, we show that EoT achieves these superior results in acost-effective manner, marking a promising advancement for efficient andcollaborative AI problem-solving.

Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication