Exchange-of-Thought: Enhancing Large Language Model Capabilities through
  Cross-Model Communication
Zhangyue Yin Qiushi Sun Cheng Chang Qipeng Guo Junqi Dai Xuanjing Huang Xipeng Qiu
Large Language Models (LLMs) have recently made significant strides in
complex reasoning tasks through the Chain-of-Thought technique. Despite this
progress, their reasoning is often constrained by their intrinsic
understanding, lacking external insights. To address this, we propose
Exchange-of-Thought (EoT), a novel framework that enables cross-model
communication during problem-solving. Drawing inspiration from network
topology, EoT integrates four unique communication paradigms: Memory, Report,
Relay, and Debate. This paper delves into the communication dynamics and volume
associated with each paradigm. To counterbalance the risks of incorrect
reasoning chains, we implement a robust confidence evaluation mechanism within
these communications. Our experiments across diverse complex reasoning tasks
demonstrate 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 a
cost-effective manner, marking a promising advancement for efficient and
collaborative AI problem-solving.