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DEPT: Decoupled Embeddings for Pre-training Language Models
- Language Model pre-training uses broad data mixtures to enhance performance across domains and languages.
- DEPT proposes a communication-efficient pre-training framework that decouples embeddings from the transformer body.
- DEPT can handle significant data heterogeneity and minimize token embedding parameters.
- DEPT improves transformer body plasticity, generalization, and overall performance.
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