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About this paper
Kevin J Liang, Weituo Hao, Dinghan Shen, Yufan Zhou, Weizhu Chen, Changyou Chen, Lawrence Carin
This paper introduces MixKD, a framework that integrates mixup data augmentation into knowledge distillation for large-scale language models. The method enables smaller student models to achieve performance close to larger teachers while significantly improving generalization, particularly in scenarios with limited labeled data.
Key insights
- Integrating mixup-based data augmentation within knowledge distillation enables student models to learn smoother decision boundaries and explore a broader representation space.
- The framework provides a theoretical basis demonstrating that augmented data for distillation can reduce the generalization gap and improve student model performance.
- MixKD is particularly effective in data-scarce scenarios, allowing student models to absorb more nuanced knowledge from the teacher despite limited original labeled data.
Summary by alphaXiv.
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