BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models [article]

Elad Ben Zaken, Shauli Ravfogel, Yoav Goldberg
2022 arXiv   pre-print
We introduce BitFit, a sparse-finetuning method where only the bias-terms of the model (or a subset of them) are being modified. We show that with small-to-medium training data, applying BitFit on pre-trained BERT models is competitive with (and sometimes better than) fine-tuning the entire model. For larger data, the method is competitive with other sparse fine-tuning methods. Besides their practical utility, these findings are relevant for the question of understanding the commonly-used
more » ... s of finetuning: they support the hypothesis that finetuning is mainly about exposing knowledge induced by language-modeling training, rather than learning new task-specific linguistic knowledge.
arXiv:2106.10199v4 fatcat:nynbsljxene2rghcapeisu75ly