title: Large Language Models as Analogical Reasoners
summary: 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.
paper_authors._id: 651cd9d8dc229d2e81840404
paper_authors.name: Michihiro Yasunaga
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-21T16:39:52.247Z
paper_authors._id: 621e9388345a1d9ab65391c3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/621e9388345a1d9ab65391c3/RxurNzyAWJOUdgeSHQi1R.jpeg
paper_authors.fullname: Michihiro Yasunaga
paper_authors.type: user
paper_authors.user: michiyasunaga
paper_authors._id: 651cd9d8dc229d2e81840405
paper_authors.name: Xinyun Chen
paper_authors._id: 651cd9d8dc229d2e81840406
paper_authors.name: Yujia Li
paper_authors._id: 651cd9d8dc229d2e81840407
paper_authors.name: Panupong Pasupat
paper_authors._id: 651cd9d8dc229d2e81840408
paper_authors.name: Jure Leskovec
paper_authors._id: 651cd9d8dc229d2e81840409
paper_authors.name: Percy Liang
paper_authors._id: 651cd9d8dc229d2e8184040a
paper_authors.name: Ed H. Chi
paper_authors._id: 651cd9d8dc229d2e8184040b
paper_authors.name: Denny Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-20T18:27:24.855Z
paper_authors._id: 65d4d62d493aeaf0bb295f1b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/vXMxaoWwFziul8TqIhQw8.jpeg
paper_authors.fullname: Denny Zhou
paper_authors.type: user
paper_authors.user: dennyzhou
paper_title: Large Language Models as Analogical Reasoners
paper_summary: 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.
paper_ai_keywords: analogical prompting analogical reasoning self-generation reasoning process large language models generality adaptability 0-shot CoT manual few-shot CoT math problem solving GSM8K MATH code generation Codeforces BIG-Bench