Large Language Models as Analogical Reasoners
Authors: name: Michihiro Yasunaga; name: Xinyun Chen; name: Yujia Li; name: Panupong Pasupat; name: Jure Leskovec; name: Percy Liang; name: Ed H. Chi; name: Denny Zhou
Published: 2023-10-03 00:57:26

Abstract: Chain-of-thought (CoT) prompting for language models demonstrates impressive
performance across reasoning tasks, but typically needs labeled exemplars of
the reasoning process. In this work, we introduce a new prompting approach,
Analogical Prompting, designed to automatically guide the reasoning process of
large language models. Inspired by analogical reasoning, a cognitive process in
which humans draw from relevant past experiences to tackle new problems, our
approach prompts language models to self-generate relevant exemplars or
knowledge in the context, before proceeding to solve the given problem. This
method presents several advantages: it obviates the need for labeling or
retrieving exemplars, offering generality and convenience; it can also tailor
the generated exemplars and knowledge to each problem, offering adaptability.
Experimental results show that our approach outperforms 0-shot CoT and manual
few-shot CoT in a variety of reasoning tasks, including math problem solving in
GSM8K and MATH, code generation in Codeforces, and other reasoning tasks in
BIG-Bench.