Title: PAL: Program-aided Language Models
Authors: Luyu Gao, Aman Madaan, Shuyan Zhou et al.
Year: 2022
arXiv: 2211.10435 (https://arxiv.org/abs/2211.10435)

Abstract:
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 this http URL .
