LLMs are primarily self-supervised with a supervised fine-tuning stage.
Supervised learning needs a human to label every example. Unsupervised learning runs on raw data — no labels needed. Self-supervised learning is a clever subset of supervised where labels come from the data itself (Stanford CS229 lecture notes; Google AI blog on self-supervision, 2022).
{image: cat.jpg, label: "cat"}"The cat sat on the ___" with target "mat"| Aspect | Supervised | Unsupervised |
|---|---|---|
| Needs labels | Yes | No |
| Goal | Predict | Discover |
| Evaluation | Clear (accuracy, F1) | Subjective |
| Data cost | High | Low |
| Typical algos | Random forest, XGBoost, neural nets | K-means, PCA, DBSCAN |
Is reinforcement learning a third type? Yes — RL uses reward signals rather than labels or raw data. RLHF combines supervised and RL.
Are LLMs supervised? Yes — self-supervised during pre-training, then supervised during fine-tuning.
Which is easier? Unsupervised needs less data prep; supervised produces more reliable outcomes.
Can I convert unsupervised into supervised? Sometimes — label a small sample, then use semi-supervised learning.
What is semi-supervised learning? Mixes a small labeled set with a large unlabeled one.
Do I need unsupervised for embeddings? Modern embedding models are self-supervised with contrastive learning.
Which paradigm is most used commercially? Supervised (labeled classification) — but self-supervised pre-training enabled the LLM boom.
Self-supervised pre-training plus supervised fine-tuning is the recipe behind every frontier LLM. Most businesses use supervised learning for targeted prediction. More ML primers on Misar Blog.
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