Papers › Fastformer: Additive Attention Can Be All You Need
Fastformer: Additive Attention Can Be All You Need
Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, Xing Xie
Transformer is a powerful model for text understanding. However, it is inefficient due to its quadratic complexity to input sequence length. Although there are many methods on Transformer acceleration, they are still either inefficient on long sequences or not effective enough. In this paper, we propose Fastformer, which is an efficient Transformer model based on additive attention. In Fastformer, instead of modeling the pair-wise interactions between tokens, we first use additive attention mechanism to model global contexts, and then further transform each token representation based on its interaction with global context representations. In this way, Fastformer can achieve effective context modeling with linear complexity. Extensive experiments on five datasets show that Fastformer is much more efficient than many existing Transformer models and can meanwhile achieve comparable or even better long text modeling performance.
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Code
Syntology Ran 4 of 4 code samples harvested from 1 repository linked to this paper; 0 have no recorded run. Of those that ran: 2 ran · violated contract; 2 ran · our draft was wrong.
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13 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
4 samples harvested; 4 ran; 0 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 3 of the 4 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from lucidrains/fast-transformer-pytorch. Some samples are identical code Syntology first harvested from another repository; for those, this paper's copy is not located and its licence is not recorded. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text Summarization | CNN / Daily Mail (Anonymized) | Fastformer | ROUGE-1 | 38.54 | #11 of 13 | Archive leaderboard | report |
| Text Summarization | CNN / Daily Mail (Anonymized) | Fastformer | ROUGE-2 | 16.22 | #11 of 13 | Archive leaderboard | report |
| Text Summarization | CNN / Daily Mail (Anonymized) | Fastformer | ROUGE-L | 36.21 | #11 of 13 | Archive leaderboard | report |
| Text Summarization | Pubmed | Fastformer | ROUGE-1 | 38.09 | #28 of 29 | Archive leaderboard | report |
| Text Summarization | Pubmed | Fastformer | ROUGE-2 | 15.44 | #28 of 29 | Archive leaderboard | report |
| Text Summarization | Pubmed | Fastformer | ROUGE-L | 34.81 | #28 of 29 | 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
Introduced by this paper: Fastformer
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