Analogical Prompting
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Google's Analogical Prompting guides LLM reasoning by having the model self-generate relevant exemplars on the fly.
Self-generated exemplars: Rather than requiring curated few-shot demonstrations, the model is prompted to recall or generate relevant analogous problems before solving the target question.
Analogical-reasoning inspiration: Draws on the cognitive-science concept of analogical reasoning, where humans solve new problems by invoking similar past cases.
No labeled exemplars needed: Unlike CoT, which requires demonstrations of the reasoning process, Analogical Prompting requires no labeled reasoning data at all.
Benchmark gains: Improves over standard CoT and zero-shot baselines across math, commonsense, and code reasoning tasks, with particularly strong gains on math word problems.
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