Annotation Entropy Predicts Per-Example Learning Dynamics in LoRA Fine-Tuning

This research discovers that LoRA fine-tuning leads to 'un-learning' on contested examples, where high annotator disagreement correlates with increased loss during training. This pattern is distinct from full fine-tuning and consistently observed across multiple encoder and decoder-only models and datasets.