In-context learning has enabled pretrained language models to achieve impressive zero- and few-shot learning performance without parameter updates. Most conventional in-context learning techniques however restrict the number of training examples, which limits their effectiveness when a large number of examples are available. In the new paper Structured Prompting: Scaling In-Context Learning to 1,000 Examples, a Microsoft Research team proposes structured p