Bridging the Confidence Gap: Temperature Scaling for Calibrating Test-Time Prompt Tuning
CoTS temperature scaling cuts test-time prompt tuning's expected calibration error from 11.90% to 5.38% on ImageNet variants while raising accuracy.
The paper proposes CoTS, a post-hoc calibration method that applies temperature scaling to minimize the confidence gap between test-time-adapted and zero-shot predictions. A weak-strong ensemble variant, E-CoTS, further exploits multiple test-time augmentations to boost accuracy while maintaining calibration. E-CoTS reduces average expected calibration error from 11.90% to 5.38% on ImageNet variants while increasing accuracy from 60.74% to 62.95%.
- Temperature scaling aligns adapted predictions with well-calibrated zero-shot ones
- Weak-strong ensemble exploits multiple test-time augmentations
- E-CoTS cuts ECE from 11.90% to 5.38% on ImageNet variants
- Accuracy rises from 60.74% to 62.95% without calibration trade-off
From the original article
1 min read at arxiv.orgTest-time prompt tuning (TPT) enables adaptation on a single test instance, achieving improved accuracy but often sacrificing calibration performance. Most existing calibration methods introduce additional regularization terms to promote dispersion across text embeddings and reduce calibration error, yet these methods often suffer from a drop in accuracy.
By Yuwei Liang · arxiv.org
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