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Relevance 7/10Training ParadigmsIntermediate6 min read
Data Augmentation
Data augmentation creates modified examples to improve model robustness.
Why it matters for annotators
Augmented data can improve coverage when real examples are sparse.
Visual mental model
Original sample -> transformations -> expanded dataset.
Examples (bad vs good)
Scenario: Real annotation scenario involving Data Augmentation
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.