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arXiv · 2110.08190

Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm

Shaoyi Huang·Dongkuan Xu·Ian E. H. Yen·Yijue Wang·Sung-en Chang·Bingbing Li·Shiyang Chen·Mimi Xie·Sanguthevar Rajasekaran·Hang Liu·Caiwen Ding
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Abstract

Conventional wisdom in pruning Transformer-based language models is that pruning reduces the model expressiveness and thus is more likely to underfit rather than overfit. However, under the trending pretrain-and-finetune paradigm, we postulate a counter-traditional hypothesis, that is: pruning increases the risk of overfitting when performed at the fine-tuning phase. In this paper, we aim to address the overfitting problem and improve pruning performance via progressive knowledge distillation with error-bound properties. We show for the first time that reducing the risk of overfitting can help the effectiveness of pruning under the pretrain-and-finetune paradigm. Ablation studies and experiments on the GLUE benchmark show that our method outperforms the leading competitors across different tasks.

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Shaoyi Huang, Dongkuan Xu, Ian E. H. Yen, Yijue Wang, Sung-en Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Sanguthevar Rajasekaran, Hang Liu, Caiwen Ding. 2021-10-15. Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm. https://arxiv.org/abs/2110.08190

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