MathDivide：通过大型语言模型改进数学推理
MathDivide: Improved mathematical reasoning by large language models
AI总结：
本文提出MathDivide提示技术，将数学问题分解为子问题并用Python代码求解，在GSM8K数据集上显著优于Math-prompter。
摘要：
大型语言模型已被证明能够处理复杂的语言和认知任务。因此，它们的应用已扩展到需要逻辑推理能力的任务，如数学。在本文中，我们提出了一种名为MathDivide的提示技术，将数学问题分解为更简单的子问题。每个子问题被表述为一个代数表达式，其值由LLM为相应代数表达式生成的Python代码计算。提供给Python代码的值是问题陈述中提供的数值。子问题的解决方案被组合在一起，以获得问题陈述的最终答案。最后，将最终答案与正确答案进行比较。如果最终答案与正确答案匹配，则将其作为输出；否则，将细化提示反馈给LLM。我们使用GSM8K数据集在闭源LLM模型和开源LLM模型上实验了这种提示技术。结果表明，MathDivide能够显著优于领先的提示技术Math-prompter。
英文摘要：
Large language models have been proven to be capable of handling complex linguistic and cognitive tasks. Therefore their usage has been extended to tasks requiring logical reasoning ability such as Mathematics. In this paper, we propose a prompting technique called MathDivide that breaks down the mathematical problem into simpler subproblems. Each of the subproblems is formulated as an algebraic expression whose value is evaluated by the Python code generated by the LLM for the corresponding algebraic expression. The values fed to the Python code are the numerical values provided in the problem statement. The solutions for the subproblems are composed together to obtain the final answer for the problem statement. Finally, the final answer is compared to the correct answer. If the final answer matches the correct answer, it is produced as output else a refinement prompt is fed to the LLM. We experiment with this prompting technique on both closed-source LLM models and open-source LLM models using GSM8K dataset. The results obtained demonstrate that MathDivide was able to significantly outperform the leading prompting technique called Math-prompter.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
