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.

使用 MathDivide 提示技术将数学问题分解为简单的子问题，由大语言模型生成相应的 Python 代码来评估代数表达式的值，并根据子问题的解组合得出最终答案，结果表明 MathDivide 显著优于领先的提示技术 Math-prompter。