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- W4387355871 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." @default.
- W4387355871 created "2023-10-05" @default.
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- W4387355871 date "2023-10-02" @default.
- W4387355871 modified "2023-10-12" @default.
- W4387355871 title "Large Language Models as Analogical Reasoners" @default.
- W4387355871 doi "https://doi.org/10.48550/arxiv.2310.01714" @default.
- W4387355871 hasPublicationYear "2023" @default.
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