Papers with movement pruning
Data Pruning for Efficient Model Pruning in Neural Machine Translation (2023.findings-emnlp)
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| Challenge: | Large-scale pre-trained language models have demonstrated encouraging performance in various NLP tasks at the cost of over-parametrized networks and high memory requirements. |
| Approach: | They combine data pruning with movement pruning for Neural Machine Translation to enable efficient fine-pruning by leveraging cross-entropy scores of individual training instances. |
| Outcome: | The proposed pruning strategy outperforms other pruning methods on a translation task and shows that training cross-entropy scores can reduce the steps required for convergence and training time. |
Pruning Pre-trained Language Models Without Fine-Tuning (2023.acl-long)
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| Challenge: | Existing methods to prune Pre-trained Language Models (PLMs) are overparameterized and require fine-tuning. |
| Approach: | They propose a pruning method that uses first-order pruning to prune PLMs while fine-tuning the remaining weights. |
| Outcome: | The proposed method outperforms first-order pruning and zero-order methods at sparsity levels. |
Layer-wise Model Pruning based on Mutual Information (2021.emnlp-main)
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| Challenge: | In spite of impressive results of neural networks, the huge model size has hindered their applications in cases where computation and memory resources are limited. |
| Approach: | They propose a method for layer-wise pruning using mutual information based feature selection in SVMs and logistic regression. |
| Outcome: | The proposed pruning strategy offers greater speedup and higher performance than weight-based pruning methods. |