Limitations of Normalization in Attention Mechanism
This paper investigates the limitations of the normalization in attention
mechanisms. We begin with a theoretical framework that enables the
identification of the model's selective ability and the geometric separation
involved in token selection. Our analysis includes explicit bounds on distances
and separation criteria for token vectors under softmax scaling. Through
experiments with pre-trained GPT-2 model, we empirically validate our
theoretical results and analyze key behaviors of the attention mechanism.
Notably, we demonstrate that as the number of selected tokens increases, the
model's ability to distinguish informative tokens declines, often converging
toward a uniform selection pattern. We also show that gradient sensitivity
under softmax normalization presents challenges during training, especially at
low temperature settings. These findings advance current understanding of
softmax-based attention mechanism and motivate the need for more robust
normalization and selection strategies in future attention architectures.
68ad76c486b21a0e2e358f2e
Timur Mudarisov
claimed_verified
2025-08-26T10:34:40.287Z
66348e8fc58f455771063d68
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/1D_v0kPviu-JBViDuzuoC.jpeg
Timur Mudarisov
user
opensapce
68ad76c486b21a0e2e358f2f
Mikhail Burtsev
claimed_verified
2025-08-26T09:49:08.448Z
639c6e978a34ed9a404c6a7b
/avatars/c98ca8c9f9ed8509c2f1bb6aa994fd57.svg
MIKHAIL BURTSEV
user
mbur
68ad76c486b21a0e2e358f30
Tatiana Petrova
68ad76c486b21a0e2e358f31
Radu State
Limitations of Normalization in Attention Mechanism
This paper investigates the limitations of the normalization in attention
mechanisms. We begin with a theoretical framework that enables the
identification of the model's selective ability and the geometric separation
involved in token selection. Our analysis includes explicit bounds on distances
and separation criteria for token vectors under softmax scaling. Through
experiments with pre-trained GPT-2 model, we empirically validate our
theoretical results and analyze key behaviors of the attention mechanism.
Notably, we demonstrate that as the number of selected tokens increases, the
model's ability to distinguish informative tokens declines, often converging
toward a uniform selection pattern. We also show that gradient sensitivity
under softmax normalization presents challenges during training, especially at
low temperature settings. These findings advance current understanding of
softmax-based attention mechanism and motivate the need for more robust
normalization and selection strategies in future attention architectures.
Theoretical and empirical analysis of softmax normalization in attention mechanisms reveals limitations in token selection and gradient sensitivity, highlighting the need for improved normalization strategies.
attention mechanisms normalization token selection softmax scaling gradient sensitivity GPT-2 geometric separation token vectors uniform selection pattern