  Large language models (LLMs) have recently demonstrated an impressive ability
to perform arithmetic and symbolic reasoning tasks, when provided with a few
examples at test time ("few-shot prompting"). Much of this success can be
attributed to prompting methods such as "chain-of-thought'', which employ LLMs
for both understanding the problem description by decomposing it into steps, as
well as solving each step of the problem. While LLMs seem to be adept at this
sort of step-by-step decomposition, LLMs often make logical and arithmetic
mistakes in the solution part, even when the problem is decomposed correctly.
In this paper, we present Program-Aided Language models (PAL): a novel approach
that uses the LLM to read natural language problems and generate programs as
the intermediate reasoning steps, but offloads the solution step to a runtime
such as a Python interpreter. With PAL, decomposing the natural language
problem into runnable steps remains the only learning task for the LLM, while
solving is delegated to the interpreter. We demonstrate this synergy between a
neural LLM and a symbolic interpreter across 13 mathematical, symbolic, and
algorithmic reasoning tasks from BIG-Bench Hard and other benchmarks. In all
these natural language reasoning tasks, generating code using an LLM and
reasoning using a Python interpreter leads to more accurate results than much
larger models. For example, PAL using Codex achieves state-of-the-art few-shot
accuracy on the GSM8K benchmark of math word problems, surpassing PaLM-540B
which uses chain-of-thought by absolute 15% top-1. Our code and data are
publicly available at http://reasonwithpal.com/ .
