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

• URL: http://arxiv.org/abs/2407.02775v1
• Date: Wed, 3 Jul 2024 03:03:30 GMT
• Title: MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models
• Authors: Ying Zhang, Ziheng Yang, Shufan Ji,
• Abstract summary: We propose a novel knowledge distillation method MLKD-BERT to distill multi-level knowledge in teacher-student framework. 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.
• Score: 4.404914701832396
• License: http://creativecommons.org/licenses/by/4.0/
• 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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