Abstract
The large size of pretrained networks makes them difficult to deploy for multiple tasks in storage-constrained settings. Diff pruning enables parameter-efficient transfer learning that scales well with new tasks. The approach learns a task-specific “diff” vector that extends the original pretrained parameters. This diff vector is adaptively pruned during training with a differentiable approximation to the L0-norm penalty to encourage sparsity. As the number of tasks increases, diff pruning remains parameter-efficient, as it requires storing only a small diff vector for each task. Since it does not require access to all tasks during training, it is attractive in on-device deployment settings where tasks arrive in stream or even from different providers. Diff pruning can match the performance of finetuned baselines on the GLUE benchmark while only modifying 0.5% of the pretrained model’s parameters per task and scales favorably in comparison to popular pruning approaches.- Anthology ID:
- 2021.acl-long.378
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4884–4896
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.378
- DOI:
- 10.18653/v1/2021.acl-long.378
- Cite (ACL):
- Demi Guo, Alexander Rush, and Yoon Kim. 2021. Parameter-Efficient Transfer Learning with Diff Pruning. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4884–4896, Online. Association for Computational Linguistics.
- Cite (Informal):
- Parameter-Efficient Transfer Learning with Diff Pruning (Guo et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.acl-long.378.pdf
- Code
- dguo98/DiffPruning + additional community code
- Data
- GLUE, QNLI, SQuAD