When Confidence Drives the Workflow: What HypeLoRA Changes About Adapter Selection
TL;DR for operators A classifier can score better on a benchmark while giving probabilities that systematically overstate or understate how often it is correct. That matters whenever confidence feeds an escalation threshold, automatic action, human-review queue, or other operational rule. LoRA adapts a frozen model through a small update built from two low-rank factors, $A$ and $B$. HypeLoRA asks whether generating those adaptations across layers with a shared hyper-network improves not just task performance but the reliability of the resulting confidence signal.1 The answer is conditional: standard LoRA does not calibrate uniformly better than full fine-tuning, and fully generated HypeLoRA remains broadly similar to ordinary LoRA. The strongest reported calibration result comes from the Transformer fixed-$A$ configuration, which freezes $A$ and generates only $B$: ECE reaches 0.100 on CoLA and 0.028 on SST-2, while task performance remains below the strongest-performing configuration. ...