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arXiv · 2505.11040

Efficient Attention via Pre-Scoring: Prioritizing Informative Keys in Transformers

Zhexiang Li·Haoyu Wang·Yutong Bao·David Woodruff
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Abstract

Efficient attention mechanisms enable long-context transformers but often miss globally important tokens, degrading modeling quality. We introduce a pre-scoring framework that assigns a query-independent global importance prior to keys before applying hierarchical approximate attention. Using clustering-based or leverage-style scoring, pre-scoring identifies structurally informative keys and restricts computation to this prioritized subset. Integrated with HyperAttention, pre-scoring substantially improves approximation quality on long-context language modeling: on ChatGLM with 131k-token contexts, perplexity decreases from 12.0 to 9.5 under a fixed interaction budget while retaining subquadratic efficiency. Clustering-based scoring consistently outperforms leverage-based selection under identical key budgets. Beyond language, replacing self-attention in Vision Transformers preserves most of the baseline accuracy, showing that the approach generalizes across modalities. We provide structural guarantees under a planted-subspace model, showing that clustering recovers the same heavy-key sets as leverage-based methods. Overall, pre-scoring improves the efficiency-accuracy trade-off of approximate attention by better prioritizing informative keys without sacrificing scalability.

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Zhexiang Li, Haoyu Wang, Yutong Bao, David Woodruff. 2025-05-16. Efficient Attention via Pre-Scoring: Prioritizing Informative Keys in Transformers. https://arxiv.org/abs/2505.11040

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