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Relevance 7/10Quality and QAIntermediate6 min read
Disagreement Mining
Disagreement mining identifies and analyzes patterns where annotators frequently diverge.
Why it matters for annotators
It surfaces unclear policies and hidden edge-case clusters.
Visual mental model
High-disagreement samples -> cluster -> policy fixes.
Examples (bad vs good)
Scenario: Real annotation scenario involving Disagreement Mining
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.