Task Format as a Factor in Adaptation: A Controlled Comparison of LoRA and Full Fine-Tuning for Prompt-Safety Classification

Summary

This study compares LoRA with full fine-tuning for prompt-safety classification, using a matched 400M encoder-decoder pair (Ettin) across three task formats and up to 15 seeds per cell. At pre-specified learning rates, full fine-tuning leads LoRA by 2-3 pp, but on the encoder the gap closes when both learning rates are chosen by the same search. Trained models beat a prompted 8B in domain yet trail it by 8-23 pp on adversarial jailbreaks and flag 27-66% of benign but harmful-looking prompts as unsafe, versus 2-3% for the 8B.