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.

通过链式推理技术，大型语言模型（LLMs）在复杂推理任务中取得了显著进展。然而，它们的推理常常受到内在理解的局限，缺乏外部洞察力。为了解决这个问题，我们提出了一种名为 “交换思路（EoT）” 的新框架，它在问题解决过程中实现了跨模型通信。EoT 从网络拓扑中汲取灵感，整合了四种独特的通信范式：记忆、报告、中继和辩论。本文深入探讨了每种范式所涉及的通信动态和通信量。为了平衡不正确推理链的风险，我们在这些通信中实现了一个健壮的置信度评估机制。我们对各种复杂推理任务进行的实验表明，EoT 明显超过了已有的基线方法，凸显了外部洞察力在增强 LLM 性能方面的价值。此外，我们还展示了 EoT 以一种具有成本效益的方式实现这些卓越结果，标志着高效而协同的人工智能问题解决的有望发展。

思想交流：通过模型协作提升大型语言模型的能力

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

Communication of signals among nodes in a complex network poses fundamental
problems of efficiency and cost. Routing of messages along shortest paths
requires global information about the topology, while spreading by diffusion,
which operates according to local topological features, is informationally
"cheap" but inefficient. We introduce a stochastic model for network
communication that combines varying amounts of local and global information
about the network topology. The model generates a continuous spectrum of
dynamics that converge onto shortest-path and random-walk (diffusion)
communication processes at the limiting extremes. We implement the model on two
cohorts of human connectome networks and investigate the effects of varying
amounts of local and global information on the network's communication cost. We
identify routing strategies that approach a (highly efficient) shortest-path
communication process with a relatively small amount of global information.
Moreover, we show that the cost of routing messages from and to hub nodes
varies as a function of the amount of global information driving the system's
dynamics. Finally, we implement the model to identify individual subject
differences from a communication dynamics point of view. The present framework
departs from the classical shortest paths vs. diffusion dichotomy, suggesting
instead that brain networks may exhibit different types of communication
dynamics depending on varying functional demands and the availability of
resources.

本文研究了复杂网络中信号节点之间的通信效率和成本问题，并引入了一种随机模型来结合关于网络拓扑的本地和全局信息，从而产生连续谱的动态，其中包括最短路径和随机游走通信过程。接着，我们在人类连接组网络的两个组别上实现了该模型，并研究了不同程度本地和全局信息对网络通信成本的影响。我们还发现了一些逼近高效的最短路径通信过程的路由策略，并展示了全局信息驱动系统动态时，对中枢节点进行消息路由的成本随全局信息的数量变化而变化。最终，我们使用该模型来从通信动态角度识别个体主体之间的差异，该研究为经典的最短路径与扩散范式之外的大脑网络中存在不同类型的通信动态提供了新的理论支持。