Active learning is a technique used during the training of classification or entity extraction models, particularly when custom-training a model. It is a method for the model to interactively select the most informative instances for labeling.

The primary benefit of active learning is that it significantly reduces the overall effort required for manually labeling data for training, making the fine-tuning process more efficient.