Redefining Transformers: How Simple Feed-Forward Neural Networks Can Mimic Attention Mechanisms for Efficient Sequence-to-Sequence Tasks

Researchers from ETH Zurich analyze the efficacy of utilizing standard shallow feed-forward networks to emulate the attention mechanism in the Transformer model, a leading architecture for sequence-to-sequence tasks. Key attention mechanism elements in the Transformer are replaced with simple feed-forward networks trained through knowledge distillation. Rigorous ablation studies and experiments with various replacement network types […]

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