Expired50% confidenceFactExact time
In 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.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
Four Practical Claude Code Commands to Double Your AI Programming Efficiency
bilibiliopenclaw养成记6/3/2026
Related Claims
VerifiedThe 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 1.84% similarUnverifiedIn 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.83% similarUnverifiedSelf-attention weights are normalized through the Softmax function, and the sum of the weights is always 1.77% similar
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