Summary of Generalizable and Stable Finetuning Of Pretrained Language Models on Low-resource Texts, by Sai Ashish Somayajula et al.
Generalizable and Stable Finetuning of Pretrained Language Models on Low-Resource Texts
by Sai Ashish Somayajula, Youwei Liang, Abhishek Singh, Li Zhang, Pengtao Xie
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recently developed regularization method for fine-tuning Pretrained Language Models (PLMs) on low-resource datasets offers a significant improvement over existing approaches. The new method, which combines attention-guided weight mixup with bi-level optimization (BLO), provides finer control over the selection of sub-networks and improves generalization while combating overfitting. This is particularly important for NLP tasks that rely heavily on PLMs, as fine-tuning these models on low-resource datasets can be challenging due to instability and overfitting issues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Pretrained Language Models have revolutionized Natural Language Processing by significantly improving task performance. However, fine-tuning these models on small or low-quality datasets can be tricky. Existing methods try to solve this problem by only updating a part of the model, while keeping the rest frozen at its initial state. But they don’t choose which parts to update very well, resulting in suboptimal results. The new method proposed here tackles this issue by treating each model weight as a mix of two things: one that’s specific to the task and another that’s inherited from the pre-trained model. This allows for more flexibility and control when updating the model. The approach is tested on various datasets and shows better performance than previous methods. |
Keywords
* Artificial intelligence * Attention * Fine tuning * Generalization * Natural language processing * Nlp * Optimization * Overfitting * Regularization
