  Although large language models demonstrate emergent abilities in solving math
word problems, there is a challenging task in complex multi-step mathematical
reasoning tasks. To improve model performance on mathematical reasoning tasks,
previous work has conducted supervised fine-tuning on open-source models by
improving the quality and quantity of data. In this paper, we propose a novel
approach, named Brain, to imitate human thought processes to enhance
mathematical reasoning abilities, using the Frontal Lobe Model to generate
plans, and then employing the Parietal Lobe Model to generate code and execute
to obtain answers. First, we achieve SOTA performance in comparison with Code
LLaMA 7B based models through this method. Secondly, we find that plans can be
explicitly extracted from natural language, code, or formal language. Our code
and data are publicly available at https://github.com/cyzhh/Brain.
