How can we perform computations over natural language representations to
solve tasks that require symbolic and numeric reasoning? We propose natural
language embedded programs (NLEP) as a unifying framework for addressing
math/symbolic reasoning, natural language understanding, and instruction
following tasks. Our approach prompts a language model to generate full Python
programs that define functions over data structures which contain natural
language representations of structured knowledge. A Python interpreter then
executes the generated code and prints the output. Despite using a task-general
prompt, we find that this approach can improve upon strong baselines across a
range of different tasks including math and symbolic reasoning, text
classification, question answering, and instruction following. We further find
the generated programs are often interpretable and enable post-hoc verification
of the intermediate reasoning steps.

提出自然语言嵌入程序作为一种统一的框架，用于解决需要符号和数值推理的任务，其中该方法促使语言模型生成包含自然语言表示结构化知识的数据结构上定义函数的完整 Python 程序，然后 Python 解释器执行生成的代码并打印输出。尽管使用了通用的提示，但我们发现此方法可以改进各种不同任务，包括数学和符号推理、文本分类、问题回答和指令跟随，并且生成的程序通常是可解释的，能够进行事后验证中间推理步骤。