Supervised learning
A type of machine learning where a model is trained on labeled data with correct answers provided.
Detailed Definition
Supervised learning refers to when a model is trained on "labeled" data—meaning the correct answers are provided. For example, the model might be given thousands of emails labeled "spam" or "not spam" and, from that, learn to spot the patterns that distinguish them. Most modern language models use a subtype called "self-supervised learning," where the model creates its own labels from raw text without manual annotation.
Learning MethodsMore in this Category
Few-Shot Learning
The ability of AI models to learn new tasks with only a small number of training examples.
Fine-tuning
A post-training technique to specialize a trained model on specific data for a particular task.
Post-training
Additional steps taken after a model's initial training is complete to make it more useful.
RLHF (reinforcement learning from human feedback)
A post-training technique to align AI models with human preferences using feedback.
Training/Pre-training
The process by which an AI model learns by analyzing massive amounts of data.
Unsupervised learning
A type of machine learning where a model is given unlabeled data to find patterns on its own.