LoRA for Sequence Classification
Low-rank adaption, or LoRA, is another parameter-efficient fine-tuning techniques to align large models to specific tasks. The core idea is to approximate updates of a large matrix by two matrices of smaller rank:
The idea works becasue in the fine-tuning stage, the data we use is very small and narrowly-focused (on some speficic domain) compared to the pretrained data, and thus we can represent the updates with smaller matrices.