In utilizing large language models (LLMs) for mathematical reasoning, addressing the errors in the reasoning and calculation present in the generated text by LLMs is a crucial challenge. In this paper, we propose a novel framework that integrates the Chain-of-Thought (CoT) method with an external tool (Python REPL). We discovered that by prompting LLMs to generate structured text in XML-like markup language, we could seamlessly integrate CoT and the external tool and control the undesired behaviors of LLMs. With our approach, LLMs can utilize Python computation to rectify errors within CoT. We applied our method to ChatGPT (GPT-3.5) to solve challenging mathematical problems and demonstrated that combining CoT and Python REPL through the markup language enhances the reasoning capability of LLMs. Our approach enables LLMs to write the markup language and perform advanced mathematical reasoning using only zero-shot prompting.

翻译：在利用大语言模型进行数学推理时，处理大语言模型生成文本中出现的推理与计算错误是一个关键挑战。本文提出了一种新型框架，将思维链方法与外部工具（Python REPL）相结合。我们发现，通过提示大语言模型生成XML类标记语言的结构化文本，能够无缝整合思维链与外部工具，并抑制大语言模型的不良行为。采用我们的方法，大语言模型可利用Python计算来修正思维链中的错误。我们将该方法应用于ChatGPT（GPT-3.5）以解决具有挑战性的数学问题，实验表明通过标记语言整合思维链与Python REPL能增强大语言模型的推理能力。我们的方法使大语言模型仅通过零样本提示即可编写标记语言并执行高级数学推理。