Current prompting approach for language model inference mainly rely on Language Model's (LLM) autonomous exploration of reasoning paths, confronts an inevitable retracing operation when erroneous routes are encountered. This is followed by the pursuit of alternative reasoning paths. However, humans are adept at abstracting optimal solutions from problems, thereby facilitating swift and precise reasoning for similar problems resolution. In light of this, we delves into the potential of harnessing expert knowledge to enhance problem-solving within LLMs. We introduce a novel paradigm, the State Machine of Thought (SMoT), which employs predefined state machines to furnish LLMs with efficient reasoning paths, thereby eliminating fruitless exploration. Furthermore, we propose a multi-agent mechanism that assigns different objectives to agents, aiming to enhance the accuracy of SMoT reasoning. The experimental results, derived from an array reasoning task, reveal that SMoT realizes an extraordinary accuracy of 95\%, surpassing the performance of the state-of-the-art baselines.

翻译：当前语言模型推理的提示方法主要依赖于语言模型自主探索推理路径，当遇到错误路径时不可避免地需要进行回溯操作，随后再寻找其他替代推理路径。然而，人类擅长从问题中抽象出最优解决方案，从而快速精确地解决类似问题。鉴于此，我们探索了利用专家知识增强大语言模型问题解决能力的潜力。我们提出了一种新颖范式——思维状态机（SMoT），该范式通过预定义的状态机为LLM提供高效推理路径，从而消除无效探索。此外，我们提出了一种多智能体机制，为不同智能体分配不同目标以提升SMoT推理的准确性。在数组推理任务上的实验结果表明，SMoT实现了95%的卓越准确率，超越了当前最先进的基线方法性能。