Annotation entropy from contested labels predicts increasing loss during LoRA fine-tuning on NLI tasks, unlike full fine-tuning.
InProceedings of the 37th International Conference on Machine Learning (ICML)
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Annotation Entropy Predicts Per-Example Learning Dynamics in LoRA Fine-Tuning
Annotation entropy from contested labels predicts increasing loss during LoRA fine-tuning on NLI tasks, unlike full fine-tuning.