Verified80% confidenceFactExact time
In-context learning (ICL) refers to the ability of large language models to perform new tasks without updating weights, simply by providing examples or instructions in the input prompt, a capability first systematically described in the GPT-3 paper
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80%
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Long-term
Relevance
7/22/2026
First Seen
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Claude Code高阶实战：打造顶级AI开发流
Related Claims
VerifiedIn-Context Learning (ICL) refers to the ability of large language models to perform new tasks without updating weights, solely by providing examples or instructions in the input prompt; this ability was first systematically described in the GPT-3 paper.76% similarVerifiedThe effectiveness of prompt engineering is rooted in the large language model's In-Context Learning capability, which enables task switching without modifying model weights.67% similarUnverifiedThis arXiv paper points out that if large language models are trained on isolated triples, they tend not to learn the surrounding context of those triples, which is necessary for multi-hop reasoning.65% similarUnverifiedThe Skills paradigm uses the large language model itself to simultaneously perform intent understanding, slot filling, and parameter structuring, achieving zero-shot intent routing without requiring additional training data.65% similarVerifiedFoundation models learn by predicting the 'next word' on massive amounts of unlabeled text using a self-supervised learning paradigm, represented by models such as the GPT series and LLaMA63% similar
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