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Asking a model to perform a task with no worked examples, relying entirely on the instruction and the model's pre-trained knowledge.
In zero-shot prompting the model is given only a description of the task. Brown et al. (2020) demonstrated that sufficiently large models do this surprisingly well, and Wei et al. (2022) showed that instruction tuning — fine-tuning on many tasks phrased as instructions — sharply improves zero-shot performance on unseen tasks.
Zero-shot is fast and clean but less reliable for unusual formats or edge cases, where a few examples help.
Primary, peer-reviewed and archival sources for this definition.
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