Papers › H-Transformer-1D: Fast One-Dimensional Hierarchical Attention for Sequences
H-Transformer-1D: Fast One-Dimensional Hierarchical Attention for Sequences
Zhenhai Zhu, Radu Soricut
We describe an efficient hierarchical method to compute attention in the Transformer architecture. The proposed attention mechanism exploits a matrix structure similar to the Hierarchical Matrix (H-Matrix) developed by the numerical analysis community, and has linear run time and memory complexity. We perform extensive experiments to show that the inductive bias embodied by our hierarchical attention is effective in capturing the hierarchical structure in the sequences typical for natural language and vision tasks. Our method is superior to alternative sub-quadratic proposals by over +6 points on average on the Long Range Arena benchmark. It also sets a new SOTA test perplexity on One-Billion Word dataset with 5x fewer model parameters than that of the previous-best Transformer-based models.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Language Modelling | One Billion Word | H-Transformer-1D Nr=16 (Base) | Number of params | 53M | #26 of 27 | Archive leaderboard | report |
| Language Modelling | One Billion Word | H-Transformer-1D Nr=16 (Base) | Validation perplexity | 23.95 | #26 of 27 | Archive leaderboard | report |
| Language Modelling | One Billion Word | H-Transformer-1D Nr=16 (Large) | Number of params | 144M | #27 of 27 | Archive leaderboard | report |
| Language Modelling | One Billion Word | H-Transformer-1D Nr=16 (Large) | Validation perplexity | 20.25 | #27 of 27 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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