Self-Supervised Learning
Learning from unlabeled data by predicting part of it from the rest.
Self-supervised learning creates training signals from the data itself — for example predicting the next word or filling in masked tokens — so models can learn from vast unlabeled corpora. It is the engine behind the pretraining of large language models.