Unverified50% confidenceOpinionExact time
The nonlinear normalization and competitive attention allocation provided by Softmax is one of the important reasons for the superior performance of Transformers
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50%
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Long-term
Relevance
9/14/2026
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UnverifiedIn the Transformer architecture, the Self-Attention layer normalizes relevance scores across all positions using the Softmax function; when the sequence length expands from a few thousand tokens to tens of thousands, the attention weight allocated to each position is greatly diluted.74% similarVerifiedIn the attention mechanism of the Transformer architecture, as sequence length increases, the attention scores of early tokens are diluted by subsequent tokens after softmax normalization, forming an attention sinking effect.72% similarVerifiedThe core computation formula for the self-attention mechanism in the Transformer architecture is Attention(Q,K,V) = softmax(QKᵀ/√d_k)V, where the softmax function normalizes the attention scores of all positions into a probability distribution summing to 1.72% similarUnverifiedDeepMind's GTrXL (Gated Transformer-XL) and Decision Transformer demonstrated the advantages of attention mechanisms in modeling long-range dependencies71% similar
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