新闻
Data Scarcity and Model Sparsity: Mixtures-of-Experts Overfit More to Repeated Data
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Mixture-of-Experts models overfit more severely than dense Transformers when training data is repeated, with performance degrading at lower repetition rates.
- 为何重要
- Engineers building language models with sparse architectures should consider this when training data must be reused due to scarcity of unique text.
- 注意
- Dropout and masking-based regularization can help but do not fully restore performance to all-unique data levels, leaving a practical gap.
- language model
- mixture-of-experts
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