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We introduce BitFit, a sparse-finetuning method where only the bias-terms ofthe model (or a subset of them) are being modified. We show that withsmall-to-medium training data, applying BitFit on pre-trained BERT models iscompetitive with (and sometimes better than) fine-tuning the entire model. Forlarger data, the method is competitive with other sparse fine-tuning methods.Besides their practical utility, these findings are relevant for the questionof understanding the commonly-used process of finetuning: they support thehypothesis that finetuning is mainly about exposing knowledge induced bylanguage-modeling training, rather than learning new task-specific linguisticknowledge.

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