Papers with language model backbone replacement problem
FaLA: Fast Linear Adaptation for Replacing Backbone Models on Edge Devices (2023.findings-emnlp)
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| Challenge: | Current NLP models heavily rely on pre-trained models, such as BERT and RoBERTa. |
| Approach: | They propose a lightweight method for personalized NLP classification tasks post-backbone replacement using a personalized matrix calculated from documents corresponding to users' old and new backbones. |
| Outcome: | The proposed method achieves over 1000 times computation reduction in Flops for backpropagation and brings the user-specific initialization for personal matrix yielding significant performance boost compared with popular transfer learning methods. |