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Bridging the Confidence Gap: Temperature Scaling for Calibrating Test-Time Prompt Tuning

Paper recorded by Signals 4 on 2026-09-15 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-15 on arXiv · recorded by Signals 4 on 2026-09-16

Category: cs.LG · 机器学习 · first seen 2026-09-16

Abstract

Test-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. Motivated by the well-calibrated nature of

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#32 most recent of 215 cs.LG papers we have recorded · ↑ newer: Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulati · ↓ older: OPEN-1B: A Fully Auditable Training Run
Cite this page: Bridging the Confidence Gap: Temperature Scaling for Calibrating Test-Time Prompt Tuning: the #32 most recent of 215 cs.LG papers we have recorded (as of 2026-09-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/bridging-the-confidence-gap-temperature-scaling-for-calibrating-test-time-prompt.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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