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 this https URL

提出了一种名为 Brain 的新方法，通过模仿人类思维过程来增强数学推理能力，在生成规划时使用额叶模型，然后利用顶叶模型生成代码并执行以获得答案，在数学推理任务中取得了最先进的性能，并发现可以明确从自然语言、代码或正式语言中提取规划。