Zero-shot learning

Zero-shot learning refers to a language model's ability to perform a task correctly given only a natural language description of what to do, without any worked examples in the prompt. Large models exhibit strong zero-shot performance on diverse tasks due to broad pre-training and instruction tuning.

Zero-shot prompting is the simplest form of in-context learning and enables rapid deployment to new tasks.