Few-Shot Learning — AI Glossary

Few-shot learning is a machine learning approach where a model learns to perform a new task from only a handful of training examples — sometimes as few as one to five. Unlike traditional machine learning that requires thousands of labeled examples, few-shot learning leverages prior knowledge from pre-training to generalize from minimal data. It is a broader concept than few-shot prompting, encompassing both in-context examples and training-time techniques for learning from scarce data.