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SPEMsoftmax\mathrm{SPEM}_{\mathrm{softmax}}

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where SPEMsoftmax\mathrm{SPEM}_{\mathrm{softmax}} is Guo and Vosoughi’s own Jensen–Shannon-divergence effect magnitude, measured by their own published protocol, on an ordinary sink-forming model, and SPEMsigmoid\mathrm{SPEM}_{\mathrm{sigmoid}} is the identical measurement on an architecture- and data-matched twin trained with sigmoid attention instead of softmax — the exact contrast Gu and colleagues’ released training pipeline was built to produce [ 3 ] . The quantity is dimensionless, bounded in the neighborhood of zero to one for any model pair where the sink-free twin is not somehow more position-sensitive than its sink-forming counterpart, and it answers a single, sharp question: of the effect magnitude the…

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SPEMsoftmax\mathrm{SPEM}_{\mathrm{softmax}}

Equation 20 · Language Models & Evaluation

Cognitive Bias Was the Label; Attention Sink Is the Suspect

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where SPEMsoftmax\mathrm{SPEM}_{\mathrm{softmax}} is Guo and Vosoughi’s own Jensen–Shannon-divergence effect magnitude, measured by their own published protocol, on an ordinary sink-forming model, and SPEMsigmoid\mathrm{SPEM}_{\mathrm{sigmoid}} is the identical measurement on an architecture- and data-matched twin trained with sigmoid attention instead of softmax — the exact contrast Gu and colleagues’ released training pipeline was built to produce [ 3 ] . The quantity is dimensionless, bounded in the neighborhood of zero to one for any model pair where the sink-free twin is not somehow more position-sensitive than its sink-forming counterpart, and it answers a single, sharp question: of the effect magnitude the…

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