Large Language Models (LLMs) have shown remarkable performance in various
basic natural language tasks, which raises hopes for achieving Artificial
General Intelligence. To better complete complex tasks, we need LLMs to program
for the task and then follow the program to generate a specific solution for
the test sample. We propose using natural language as a new programming
language to describe task procedures, making them easily understandable to both
humans and LLMs. LLMs are capable of directly generating natural language
programs, but these programs may still contain factual errors or incomplete
steps. Therefore, we further propose the Learning to Program (LP) method to ask
LLMs themselves to learn natural language programs from the training dataset of
complex tasks and then use the learned program to guide inference. Our
experiments on the AMPS (high school math) and Math (competition mathematics
problems) datasets demonstrate the effectiveness of our approach. When testing
ChatGPT on 10 tasks from the AMPS dataset, our LP method's average performance
outperformed the direct zero-shot test performance by 18.3$\%$. We release our
code at https://github.com/microsoft/NaturalLanguageProgram.

使用自然语言作为一种新的编程语言来描述任务过程，让其易于被人类和 LMMs 理解，并进一步借助 Learning to Program (LP) 方法通过学习来自训练集中的自然语言程序，优化复杂任务的问题解决能力，该方法在 AMPS 和数学两个数据集上的有效性得到验证，证明其性能超过直接零样本测试表现 18.3%。