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Nyströmformer: A Nyström-based Algorithm for Approximating Self-Attention

AAAI 2021 · Architectures and efficiency › Attention and transformers · approach: Sparse efficient transformers for long context · 342 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace standard self-attention with Nyström approximationBERT-base fine-tuning · SST-2 validationaccuracy: 90.0 -> 91.4Helped
Replace standard self-attention with Nyström approximationBERT-base fine-tuning · QNLI validationaccuracy: 90.3 -> 88.7Hurt
Replace standard self-attention with Nyström approximationBERT-base fine-tuning · QQP validationF1: 87.3 -> 86.3Hurt
Replace standard self-attention with Nyström approximationBERT-base fine-tuning · IMDB validationaccuracy: 93.3 -> 93.2No clear effect
Replace standard self-attention with Nyström approximationTransformer on LRA · LRA averageaverage classification accuracy: 58.77 -> 58.95No clear effect
Use Nyström attention rather than other efficient attention methodsTransformer on LRA · LRA averageaverage classification accuracy: +3.91%, +3.36%, +5.32%Helped
Use Nyström attention rather than standard self-attentionSelf-attention module · Sequence length 8192memory (MB): 10233 (1×) -> 450 (22.8×)Helped
Use Nyström attention rather than standard self-attentionSelf-attention module · Sequence length 8192time (ms): 155.4 (1×) -> 12.3 (12.7×)Helped
Use 32 rather than 64 landmarks (ablation)Nyströmformer · Self-attention module, sequence length 8192memory (MB): 450 (22.8×) -> 383 (26.7×)Helped
Use 32 rather than 64 landmarks (ablation)Nyströmformer · Self-attention module, sequence length 8192time (ms): 12.3 (12.7×) -> 11.5 (13.4×)Helped
Fine-tune using sequence length 1024 rather than 512 (ablation)Nyströmformer · IMDB reviewsaccuracy: 93.0 vs. 93.2No clear effect

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