The paper analyzes the effectiveness of using shallow feed-forward networks to mimic the attention mechanism in the Transformer model. Results show that these "attentionless Transformers" can rival the performance of the original architecture, highlighting the potential to streamline complex architectures for sequence-to-sequence tasks.

https://arxiv.org/abs//2311.10642

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