MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models

Ying Zhang; Ziheng Yang; Shufan Ji

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arXiv · 2407.02775

MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models

Ying Zhang·Ziheng Yang·Shufan Ji

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Abstract

Knowledge distillation is an effective technique for pre-trained language model compression. Although existing knowledge distillation methods perform well for the most typical model BERT, they could be further improved in two aspects: the relation-level knowledge could be further explored to improve model performance; and the setting of student attention head number could be more flexible to decrease inference time. Therefore, we are motivated to propose a novel knowledge distillation method MLKD-BERT to distill multi-level knowledge in teacher-student framework. Extensive experiments on GLUE benchmark and extractive question answering tasks demonstrate that our method outperforms state-of-the-art knowledge distillation methods on BERT. In addition, MLKD-BERT can flexibly set student attention head number, allowing for substantial inference time decrease with little performance drop.

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Ying Zhang, Ziheng Yang, Shufan Ji. 2024-07-03. MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models. https://arxiv.org/abs/2407.02775

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