Continual Calibration: Coverage Can Collapse Before Accuracy in Lifelong LLM Fine-Tuning
Continual learning for large language models is typically evaluated through accuracy retention under sequential fine-tuning. We argue that this perspective is incomplete, because uncertainty reliability can degrade earlier and more sharply than top-1 performance. We study this empirically by measuring conformal coverage and calibration error on sequentially fine-tuned models across three model families and eight task sequences drawn primarily from classification and multiple-choice benchmarks. Across the classification-style settings we study, coverage loss exceeds accuracy loss by a factor of roughly \(3.4\times \pm 0.5\times\) on average across seeds; in the most pronounced case, covera...
| Authors | Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma |
|---|---|
| Primary category | cs.LG |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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