Confidence Calibration under Ambiguous Ground Truth
📰 ArXiv cs.AI
Confidence calibration fails when annotators disagree on ground truth labels, leading to miscalibration despite appearing well-calibrated under conventional evaluation
Action Steps
- Recognize the limitation of traditional confidence calibration methods
- Understand the impact of annotator disagreement on model calibration
- Develop strategies to address miscalibration, such as using annotator distributions or alternative evaluation metrics
Who Needs to Know This
Machine learning engineers and researchers benefit from understanding this concept to improve model calibration and reliability, especially when working with ambiguous or uncertain data
Key Insight
💡 Confidence calibration assumes unique ground-truth labels, but this assumption fails when annotators genuinely disagree
Share This
🚨 Confidence calibration can fail when annotators disagree! 🤔
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