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Although large language models can be prompted for both zero- and few-shotlearning, performance drops significantly when no demonstrations are available.In this paper, we introduce Z-ICL, a new zero-shot method that closes the gapby constructing pseudo-demonstrations for a given test input using a raw textcorpus. Concretely, pseudo-demonstrations are constructed by (1) finding thenearest neighbors to the test input from the corpus and pairing them withrandom task labels, and (2) applying a set of techniques to reduce the amountof direct copying the model does from the resulting demonstrations. Evaluationon nine classification datasets shows that Z-ICL outperforms previous zero-shotmethods by a significant margin, and is on par with in-context learning withlabeled training data in the few-shot setting. Overall, Z-ICL provides asignificantly higher estimate of the zero-shot performance levels of a model,and supports future efforts to develop better pseudo-demonstrations thatfurther improve zero-shot results.

Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations