Rethinking Attention: Exploring Shallow Feed-Forward Neural Networks as
  an Alternative to Attention Layers in Transformers
This work presents an analysis of the effectiveness of using standard shallow
feed-forward networks to mimic the behavior of the attention mechanism in the
original Transformer model, a state-of-the-art architecture for
sequence-to-sequence tasks. We substitute key elements of the attention
mechanism in the Transformer with simple feed-forward networks, trained using
the original components via knowledge distillation. Our experiments, conducted
on the IWSLT2017 dataset, reveal the capacity of these "attentionless
Transformers" to rival the performance of the original architecture. Through
rigorous ablation studies, and experimenting with various replacement network
types and sizes, we offer insights that support the viability of our approach.
This not only sheds light on the adaptability of shallow feed-forward networks
in emulating attention mechanisms but also underscores their potential to
streamline complex architectures for sequence-to-sequence tasks.
655ac5b92790287702738334
Vukasin Bozic
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2023-11-20T13:42:43.019Z
655a45a327902877025f0567
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Bozic
user
Vukasin
655ac5b92790287702738335
Danilo Dordervic
claimed_verified
2023-11-20T13:42:45.564Z
655b5fdac619b485319e353b
https://cdn-avatars.huggingface.co/v1/production/uploads/655b5fdac619b485319e353b/_ZQvyTdhG6qiKrSJMcIj1.jpeg
Danilo Dordevic
user
danilodjor
655ac5b92790287702738336
Daniele Coppola
655ac5b92790287702738337
Joseph Thommes
Rethinking Attention: Exploring Shallow Feed-Forward Neural Networks as
  an Alternative to Attention Layers in Transformers
This work presents an analysis of the effectiveness of using standard shallow
feed-forward networks to mimic the behavior of the attention mechanism in the
original Transformer model, a state-of-the-art architecture for
sequence-to-sequence tasks. We substitute key elements of the attention
mechanism in the Transformer with simple feed-forward networks, trained using
the original components via knowledge distillation. Our experiments, conducted
on the IWSLT2017 dataset, reveal the capacity of these "attentionless
Transformers" to rival the performance of the original architecture. Through
rigorous ablation studies, and experimenting with various replacement network
types and sizes, we offer insights that support the viability of our approach.
This not only sheds light on the adaptability of shallow feed-forward networks
in emulating attention mechanisms but also underscores their potential to
streamline complex architectures for sequence-to-sequence tasks.
Shallow feed-forward networks can emulate the performance of the attention mechanism in Transformers, as demonstrated by their competitive results on sequence-to-sequence tasks using knowledge distillation.
Transformer attention mechanism shallow feed-forward networks knowledge distillation IWSLT2017 ablation studies