aiqbench analysis
This paper theoretically and empirically demonstrates that softmax normalization in attention mechanisms degrades a model's ability to distinguish informative tokens as selection increases, often leading to uniform distribution patterns. The study also highlights significant gradient sensitivity issues during training, particularly at low temperature settings, which challenges current optimization strategies. These findings suggest a critical need for more robust normalization and selection strategies to improve the geometric separation and selective capabilities of future attention architectures.
Limitations of Normalization in Attention Mechanism
arXiv:2508.17821v3 Announce Type: replace Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2508.17821 [cs.LG]
(or arXiv:2508.17821v3 [cs.LG] for this version)
Full text under the publisher's open licence · 156 words
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