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Random Feature Attention

ICLR 2021 · Architectures and efficiency › Attention and transformers · approach: Linear attention approximations · 116 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add a learned sigmoid gate to Gaussian RFA (ablation)RFA-Gaussian · WikiText-103 small and big testperplexity: 35.7 -> 32.7; 27.5 -> 25.0Helped
pass the last hidden state to the next mini-batch (ablation)RFA-Gate-Gaussian · WikiText-103 testperplexity: 32.7 -> 30.5; 25.0 -> 23.5Helped
replace Gaussian features with arc-cosine features (ablation)RFA-Gaussian and RFA-Gate-Gaussian · WikiText-103perplexity: 32.7 -> 34.0; 25.0 -> 26.3Hurt
replace Gaussian features with arc-cosine features (ablation)RFA-Gaussian · WMT14 EN-DE, EN-FR, and IWSLT14 DE-ENBLEU: 28.0 -> 28.1; 39.2 -> 38.9; 34.5 -> 34.4Mixed
replace Gaussian random features with ELU+1 features (ablation)RFA-Gaussian · WikiText-103 testperplexity: 35.7 vs 40.1Hurt
replace Gaussian random features with ELU+1 features (ablation)RFA-Gaussian · WMT14 EN-DEBLEU: 28.0 vs 21.3Hurt
increase cross-attention feature size from 128 to 256 (ablation)RFA-Gaussian, causal feature size 128 · WMT14 EN-DE developmentBLEU: 24.9 -> 25.8Helped
increase cross-attention feature size from 256 to 512 (ablation)RFA-Gaussian, causal feature size 128 · WMT14 EN-DE developmentBLEU: 25.8 -> 26.0No clear effect
increase causal-attention feature size from 64 to 128 (ablation)RFA-Gaussian, cross feature size 256 · WMT14 EN-DE developmentBLEU: 25.3 -> 25.8Helped
increase causal-attention feature size from 128 to 256 (ablation)RFA-Gaussian, cross feature size 256 · WMT14 EN-DE developmentBLEU: 25.8 -> 25.8No clear effect
use a pool of 200 random projection matrices during training rather than one (ablation)RFA-Gaussian · WMT14 EN-DE developmentBLEU: 24.0 -> 25.8Helped
use Gaussian RFA instead of softmax attentionTransformer · WMT14 EN-DE, EN-FR, and IWSLT14 DE-ENBLEU: 28.1 -> 28.0; 39.0 -> 39.2; 34.6 -> 34.5No clear effect

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