In this paper, we investigate how to harness large language models (LLMs) to solve mathematical problems both quickly and accurately. Specifically, we demonstrate the effectiveness of classifying problems into distinct categories and applying category-specific problem-solving strategies to enhance the math performance of LLMs. We develop a straightforward machine learning model for problem categorization and show that its accuracy can be significantly improved through the creation of well-designed training datasets. We believe that our approach works by helping reduce hallucinations in LLMs, which is a critical step toward unlocking their potential to tackle advanced mathematical problems.

翻译：本文研究如何利用大型语言模型（LLMs）快速且准确地解决数学问题。具体而言，我们通过将问题划分为不同类别并应用针对特定类别的问题求解策略，证明了该方法能有效提升LLMs的数学求解性能。我们开发了一个简洁的机器学习模型用于问题分类，并表明通过构建精心设计的训练数据集可显著提升其分类准确率。我们认为，该方法通过帮助减少LLMs的幻觉现象而发挥作用，这是释放其处理高级数学问题潜力的关键步骤。