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Large language models have exhibited intriguing in-context learningcapability, achieving promising zero- and few-shot performance without updatingthe parameters. However, conventional in-context learning is usually restrictedby length constraints, rendering it ineffective to absorb supervision from alarge number of examples. In order to go beyond few shots, we introducestructured prompting that breaks the length limit and scales in-contextlearning to thousands of examples. Specifically, demonstration examples areseparately encoded with well-designed position embeddings, and then they arejointly attended by the test example using a rescaled attention mechanism. Sowe can scale the number of exemplars with linear complexity instead ofquadratic complexity with respect to length. Experimental results on a diverseset of tasks show that our approach improves end-task performance and reducesevaluation variance over conventional in-context learning as the number ofdemonstration examples increases. Code has been released athttps://aka.ms/structured-prompting.

Structured Prompting: Scaling In-Context Learning to 1,000 Examples