Few-Shot Learning

The ability of a model to learn a new task or adapt to new examples from just a small number of demonstrations, typically between two and around twenty. In the context of large language models, few-shot learning often refers to providing a handful of examples within the prompt itself, allowing the model to infer the pattern without any weight updates.
See also: in-context learning, zero-shot learning, prompt engineering.