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Less is More: Task-aware Layer-wise Distillation for Language Model Compression , url =

Liang, Chen, Zuo, Simiao, Zhang, Qingru, He, Pengcheng, Chen, Weizhu, Zhao, Tuo , urldate = · arXiv (Cornell University) · DOI 10.48550/arxiv.2210.01351 · arXiv 2210.01351

1 Pith paper cite this work, alongside 14 external citations. Polarity classification is still indexing.

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cs.LG 1

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2026 1

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Contribution Weights: A Geometrical Analysis of Self-Attention Transformers

cs.LG · 2026-05-29 · unverdicted · novelty 6.0

Contribution Weights combine attention, value magnitude, and directional alignment to measure token influence more faithfully than attention alone, and show attention sinks actively suppress information via a convex sink-rate to output-norm relationship.

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  • Contribution Weights: A Geometrical Analysis of Self-Attention Transformers cs.LG · 2026-05-29 · unverdicted · none · ref 104

    Contribution Weights combine attention, value magnitude, and directional alignment to measure token influence more faithfully than attention alone, and show attention sinks actively suppress information via a convex sink-rate to output-norm relationship.