The content discusses the concept of transfer capability distillation, where vanilla transformers are used as teachers to enhance the performance of MoE models. The author explains the importance of transfer capability in downstream task performance and presents experimental results supporting their method.
The content highlights the differences between MoE and vanilla transformers, emphasizing the impact of transfer capability on model performance. It introduces a novel approach to address this issue through distillation, showcasing significant improvements in downstream tasks. The experiments conducted validate the effectiveness of transfer capability distillation in enhancing MoE model performance.
Additionally, the content explores the limitations of current methods and proposes future research directions to further understand and improve transfer capability distillation. Overall, it provides valuable insights into optimizing transformer models for better performance.
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arxiv.org
Key Insights Distilled From
by Xin Lu,Yanya... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2403.01994.pdfDeeper Inquiries