Deconstructing Attention: Investigating Design Principles for Effective
  Language Modeling
The success of Transformer language models is widely credited to their
dot-product attention mechanism, which interweaves a set of key design
principles: mixing information across positions (enabling multi-token
interactions), sequence-dependent activations (where attention weights adapt to
each input), a specific mathematical form (dot-product similarities plus
softmax weighting), and coupling of queries and keys to evolving hidden states
(grounding attention in the current layer). However, the necessity of each of
these principles remains largely untested. In this work, we systematically
deconstruct attention by designing controlled variants that selectively relax
these principles, applied both uniformly across all layers and in hybrid
architectures where only some layers retain standard attention. Our empirical
analysis reveals that mechanisms for mixing tokens are indispensable, as their
absence collapses models to near-random behavior, while the exact mathematical
form and sequence dependency can be substantially relaxed, especially when
preserved in just a subset of layers. Surprisingly, even variants that fail in
isolation can achieve robust performance when interleaved with standard
attention, highlighting a cooperative effect. These findings deepen our
understanding of what truly underpins attention's effectiveness and open new
avenues for simplifying language models without sacrificing performance.
68ee24d79b77b5223f666143
Huiyin Xue
68ee24d79b77b5223f666144
Nafise Sadat Moosavi
68ee24d79b77b5223f666145
Nikolaos Aletras
Deconstructing Attention: Investigating Design Principles for Effective
  Language Modeling
The success of Transformer language models is widely credited to their
dot-product attention mechanism, which interweaves a set of key design
principles: mixing information across positions (enabling multi-token
interactions), sequence-dependent activations (where attention weights adapt to
each input), a specific mathematical form (dot-product similarities plus
softmax weighting), and coupling of queries and keys to evolving hidden states
(grounding attention in the current layer). However, the necessity of each of
these principles remains largely untested. In this work, we systematically
deconstruct attention by designing controlled variants that selectively relax
these principles, applied both uniformly across all layers and in hybrid
architectures where only some layers retain standard attention. Our empirical
analysis reveals that mechanisms for mixing tokens are indispensable, as their
absence collapses models to near-random behavior, while the exact mathematical
form and sequence dependency can be substantially relaxed, especially when
preserved in just a subset of layers. Surprisingly, even variants that fail in
isolation can achieve robust performance when interleaved with standard
attention, highlighting a cooperative effect. These findings deepen our
understanding of what truly underpins attention's effectiveness and open new
avenues for simplifying language models without sacrificing performance.
Systematic analysis of attention mechanisms in Transformer models shows that token mixing is essential, while other aspects like sequence dependency and mathematical form can be relaxed or interleaved to maintain performance.
Transformer language models dot-product attention mechanism key design principles multi-token interactions sequence-dependent activations dot-product similarities softmax weighting queries keys hidden states controlled variants hybrid architectures empirical analysis token mixing cooperative effect