DenseFormer by EPFL Researchers: Enhancing Transformer Efficiency with Depth-Weighted Averages for Superior Language Modeling Performance and Speed

The transformer architecture has improved natural language processing, with recent advancements achieved through scaling efforts from millions to billion-parameter models. However, larger models’ increased computational cost and memory footprint limit their practicality, benefiting only a few major corporations. Extending training duration necessitates larger datasets, which is challenging as even extensive datasets become insufficient. Observations indicate […]

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