Papers with low-resource and unsupervised settings
Task-specific Compression for Multi-task Language Models using Attribution-based Pruning (2023.findings-eacl)
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| Challenge: | Existing compression methods for multi-task language models use large number of parameter parameters even when performing only a specific task. |
| Approach: | They propose a training-free compression method for multi-task language models using pruning method . they use an attribution method to determine which neurons are essential for performing a specific task . |
| Outcome: | The proposed method outperforms baseline pruning methods on six widely-used datasets. |