@inproceedings{ben-zaken-etal-2022-bitfit,
title = "{B}it{F}it: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models",
author = "Ben Zaken, Elad and
Goldberg, Yoav and
Ravfogel, Shauli",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-short.1",
doi = "10.18653/v1/2022.acl-short.1",
pages = "1--9",
abstract = "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.",
}
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%0 Conference Proceedings
%T BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models
%A Ben Zaken, Elad
%A Goldberg, Yoav
%A Ravfogel, Shauli
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F ben-zaken-etal-2022-bitfit
%X 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.
%R 10.18653/v1/2022.acl-short.1
%U https://aclanthology.org/2022.acl-short.1
%U https://doi.org/10.18653/v1/2022.acl-short.1
%P 1-9
Markdown (Informal)
[BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models](https://aclanthology.org/2022.acl-short.1) (Ben Zaken et al., ACL 2022)
ACL