A training method in which the model picks the unlabeled examples it is least sure about and asks a person to label them, so fewer labels teach it more.
Active Learning reduces the cost of data labeling. Instead of having human experts review every document, the system identifies the edge cases it is least certain about and routes only those to people for labeling, which shortens the training loop.
See also Large Language Model, Fine-Tuning and Model Inference.