Summary of Mlkd-bert: Multi-level Knowledge Distillation For Pre-trained Language Models, by Ying Zhang and Ziheng Yang and Shufan Ji
MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models
by Ying Zhang, Ziheng Yang, Shufan Ji
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel knowledge distillation method, MLKD-BERT, improves upon existing techniques by exploring relation-level knowledge and offering flexible settings for student attention heads. By distilling multi-level knowledge in a teacher-student framework, MLKD-BERT outperforms state-of-the-art methods on the BERT model, as demonstrated through extensive experiments on the GLUE benchmark and extractive question answering tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MLKD-BERT is a new way to shrink language models like BERT. It’s better than current methods because it uses more information and lets you adjust some settings to make it faster or slower. The goal is to make smaller versions of big language models that can still do their job well. By testing MLKD-BERT on lots of examples, scientists found out that it really works – it’s better than other ways to shrink BERT. |
Keywords
» Artificial intelligence » Attention » Bert » Knowledge distillation » Question answering
