← AI Terminology
Weak Supervision
Weak supervision builds training labels from noisy programmatic sources (heuristics, distant supervision, other models) instead of only hand-labeled gold data.
Snorkel-style systems popularised combining labeling functions.
Snorkel-style systems popularised combining labeling functions.
Why It Matters in AI
Gold labels do not scale. Weak methods create large silver datasets for classifiers and sometimes LLM SFT. Managing noise via label models is a practical data-centric skill.
Key Points
| Aspect | Description |
|---|---|
| Con | Systematic biases; leakage |
| Pro | Scale; rapid iteration |
| Tools | Snorkel, programmatic ETL label pipelines |
| Combine | Label models estimate source accuracies |
| Related | Distant supervision, synthetic data |
| Sources | Rules, regexes, KB alignment, teacher models |
Simple Analogy
Many imperfect TAs marking homework with different rubrics, then a system estimating whom to trust on each question.
Common Usage Examples
- Write labeling functions over tickets
- Snorkel generative label model
- Train classifier on probabilistic labels
- Audit errors for rule blind spots
Summary
In short: Weak supervision creates noisy programmatic labels at scale — trading perfection for coverage when gold annotation is scarce.