Self-Supervised Learning

A training approach where the model generates its own supervisory signal directly from unlabeled data, without relying on human-provided labels. A common example is predicting a masked word from its context — the surrounding text provides the label for free. Self-supervised learning is the foundation of most modern large language models, enabling them to extract rich representations from vast amounts of raw text without expensive annotation.
See also: pre-training, masked language modeling.