Large Language Models as Analogical Reasoners
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.
651cd9d8dc229d2e81840404
Michihiro Yasunaga
claimed_verified
2025-02-21T16:39:52.247Z
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/avatars/d009c811dc63224f8eba1889fefad04b.svg
Michihiro Yasunaga
user
michiyasunaga
651cd9d8dc229d2e81840405
Xinyun Chen
651cd9d8dc229d2e81840406
Yujia Li
651cd9d8dc229d2e81840407
Panupong Pasupat
651cd9d8dc229d2e81840408
Jure Leskovec
651cd9d8dc229d2e81840409
Percy Liang
651cd9d8dc229d2e8184040a
Ed H. Chi
651cd9d8dc229d2e8184040b
Denny Zhou
claimed_verified
2024-02-20T18:27:24.855Z
65d4d62d493aeaf0bb295f1b
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/vXMxaoWwFziul8TqIhQw8.jpeg
Denny Zhou
user
dennyzhou
Large Language Models as Analogical Reasoners
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.
Analogical Prompting enhances language model performance in reasoning tasks by self-generating relevant exemplars without labeled data.
Analogical Prompting analogical reasoning generative models GSM8K MATH Codeforces BIG-Bench reasoning tasks code generation