OptiPrune: Effective Pruning Approach for Every Target Sparsity (2025.coling-main)
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| Challenge: | Existing methods for model pruning only perform optimally within specific sparsity ranges. |
| Approach: | They propose a pruning method that reduces model size by eliminating redundant parameters . they compare it with OptiPrune, which adapts non-uniform sparsity with adaptive deviation . |
| Outcome: | The proposed method reduces model size and maintains performance despite large size and high computational demands. |
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| Challenge: | Existing pruning methods require inefficient retraining for billion-scale LLMs or rely on heuristicically designed metrics to determine pruning masks, leading to performance degradation. |
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| Challenge: | Pre-trained language models often contain a vast amount of parameters, posing nontrivial requirements for storage and computation. |
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Pruning Pre-trained Language Models Without Fine-Tuning (2023.acl-long)
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| Challenge: | Structured pruning has been extensively studied on monolingual pre-trained models . but little attention has been paid to evaluating the effectiveness of structured pruning on multilingual models. |
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