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Relevance 8/10Operations and WorkflowIntermediate5 min read
Active Learning
Active learning selects uncertain samples for annotation to improve model learning efficiency.
Why it matters for annotators
It concentrates annotator effort on high-information examples.
Visual mental model
Model uncertainty -> sample selection -> annotation -> retrain.
Examples (bad vs good)
Scenario: Real annotation scenario involving Active Learning
Bad: Labeling quickly without applying project rubric.
Good: Applying rubric criteria, documenting rationale, and escalating uncertainty.
Common mistakes
- Skipping guideline details for edge cases.
- Applying inconsistent criteria across similar samples.
- Avoiding escalation even when uncertain.
Submission checklist
- Read the latest guideline update before each batch.
- Apply rubric dimensions explicitly in each decision.
- Escalate ambiguous items with concise rationale.