Large Language Models as Analogical Reasoners

Published: 16 Jan 2024, Last Modified: 17 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: large language model, prompting, analogical reasoning, reasoning
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TL;DR: We introduce Analogical Prompting, a new language model prompting approach that self-generates relevant reasoning exemplars for solving problems. We show that this outperforms previous chain-of-thought prompting methods.
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.
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Primary Area: general machine learning (i.e., none of the above)
Submission Number: 4399