Papers with downstream fine-tuning approach
Know Where You’re Going: Meta-Learning for Parameter-Efficient Fine-Tuning (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on parameter-efficient fine-tuning methods require additional measures after pre-training and before fine-uning. |
| Approach: | They propose to take parameter-efficient fine-tuning into consideration after pre-training and before fine-uning and use meta-learning to prime a model specifically for parameter-efficiency. |
| Outcome: | The proposed method improves on a pre-trained model with certain modifications and achieves 4.96 points on cross-lingual NER fine-tuning. |