What does “Knowledge Cutoff” mean?

Training approaches that let a model keep learning from new data without forgetting prior knowledge.

Training a smaller model to imitate the outputs of a larger model.

Techniques that create modified training examples to improve model generalization.

A shift in real-world input data over time that can degrade model performance.

Continuing training on domain-specific data to adapt a pre-trained model to a specific task.

How well a model performs on new, unseen data outside the training set.