  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 process 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.
