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

• URL: http://arxiv.org/abs/2106.10199v2
• Date: Tue, 22 Jun 2021 17:02:36 GMT
• Title: BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models
• Authors: Elad Ben Zaken, Shauli Ravfogel, Yoav Goldberg
• Abstract summary: We show that fine-tuning only the bias terms (or a subset of the bias terms) of pre-trained BERT models is competitive with (and sometimes better than) fine-tuning the entire model. They support the hypothesis that finetuning is mainly about exposing knowledge induced by language-modeling training, rather than learning new task-specific linguistic knowledge.
• Score: 51.53936551681613
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We show that with small-to-medium training data, fine-tuning only the bias terms (or a subset of the bias terms) of pre-trained BERT models is competitive with (and sometimes better than) fine-tuning the entire model. For larger data, bias-only fine-tuning 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.

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