Pruning Pre-trained Language Models with Principled Importance and Self-regularization (2023.findings-acl)
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| Challenge: | Pre-trained language models often contain a vast amount of parameters, posing nontrivial requirements for storage and computation. |
| Approach: | They propose a pruning method where model prediction is regularized by the latest checkpoint with increasing sparsity throughout pruning. |
| Outcome: | The proposed approach is effective at sparsity levels, and can be applied to natural language understanding, question answering, and data-to-text generation tasks. |
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