Papers with Sharpness-Aware Minimization
Mutual-pairing Data Augmentation for Fewshot Continual Relation Extraction (2025.naacl-long)
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Nguyen Hoang Anh, Quyen Tran, Thanh Xuan Nguyen, Nguyen Thi Ngoc Diep, Linh Ngo Van, Thien Huu Nguyen, Trung Le
| Challenge: | Existing methods for Few-shot Continual Relation Extraction struggle with catastrophic forgetting and overfitting. |
| Approach: | They propose a method that transforms single input sentences into complex texts by integrating old and new data. |
| Outcome: | The proposed method sharpens model focus and improves model performance . it also uncovers fascinating behaviors of Sharpness-Aware Minimization (SAM) in Few-shot Continual Learning. |
GA-SAM: Gradient-Strength based Adaptive Sharpness-Aware Minimization for Improved Generalization (2022.emnlp-main)
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| Challenge: | Recent studies show flat minima tend to imply better generalization abilities . however, it has some difficulty implying SAM to some natural language tasks . |
| Approach: | They propose a flatness-aware minimization algorithm that can be applied to natural language tasks . they propose to use parameter corruptions to explain why flat minima generalize better . |
| Outcome: | The proposed algorithm can generalize better for flat minima that are robust against corruptions or perturbations. |
Sharpness-Aware Minimization Improves Language Model Generalization (2022.acl-long)
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| Challenge: | Comparatively little work has been done to improve the generalization of language models . recent work shows that Sharpness-Aware Minimization (SAM) can improve generalization without much computational overhead. |
| Approach: | They propose a Sharpness-Aware Minimization procedure that encourages convergence to flatter minima to improve generalization of language models without much computational overhead. |
| Outcome: | The proposed Sharpness-Aware Minimization procedure can improve language models without much computational overhead. |