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Relevance 6/10Operations and WorkflowIntermediate6 min read
Near-Duplicate Detection
Near-duplicate detection finds highly similar samples that are not exact matches.
Why it matters for annotators
It prevents subtle redundancy that inflates confidence in evaluation.
Visual mental model
Similarity scoring -> cluster near-duplicates -> filter.
Examples (bad vs good)
Scenario: Real annotation scenario involving Near-Duplicate Detection
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.