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Masking as an Efficient Alternative to Finetuning for Pretrained Language Models

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arxiv 2004.12406 v2 pith:M7HNUOVL submitted 2020-04-26 cs.CL

Mengjie Zhao , Tao Lin , Fei Mi , Martin Jaggi , Hinrich Schütze This is my paper · ORCID
classification cs.CL
keywords finetuningmaskingmodelsefficientlanguagepretrainedtasksalternative
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We present an efficient method of utilizing pretrained language models, where we learn selective binary masks for pretrained weights in lieu of modifying them through finetuning. Extensive evaluations of masking BERT and RoBERTa on a series of NLP tasks show that our masking scheme yields performance comparable to finetuning, yet has a much smaller memory footprint when several tasks need to be inferred simultaneously. Through intrinsic evaluations, we show that representations computed by masked language models encode information necessary for solving downstream tasks. Analyzing the loss landscape, we show that masking and finetuning produce models that reside in minima that can be connected by a line segment with nearly constant test accuracy. This confirms that masking can be utilized as an efficient alternative to finetuning.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  3. SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling

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  4. GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation

    cs.LG 2025-08 conditional novelty 4.0 of 10

    GEM selects fine-tuning parameters by gradient-to-weight ratio and distributes the budget by layer entropy, reaching 0.1% parameter updates with small accuracy gains on several NLP tasks.

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    A survey and comparison of Parameter-Efficient Continual Fine-Tuning methods for adapting large pre-trained models to sequential tasks without catastrophic forgetting.

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