Papers › Pay Attention when Required
Pay Attention when Required
Swetha Mandava, Szymon Migacz, Alex Fit Florea
Transformer-based models consist of interleaved feed-forward blocks - that capture content meaning, and relatively more expensive self-attention blocks - that capture context meaning. In this paper, we explored trade-offs and ordering of the blocks to improve upon the current Transformer architecture and proposed PAR Transformer. It needs 35% lower compute time than Transformer-XL achieved by replacing ~63% of the self-attention blocks with feed-forward blocks, and retains the perplexity on WikiText-103 language modelling benchmark. We further validated our results on text8 and enwiki8 datasets, as well as on the BERT model.
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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 | Text8 | PAR Transformer 24B | Bit per Character (BPC) | 1.18 | #14 of 24 | Archive leaderboard | report |
| Language Modelling | WikiText-103 | PAR Transformer Large | Test perplexity | 18.4 | #35 of 89 | Archive leaderboard | report |
| Language Modelling | WikiText-103 | PAR Transformer Base | Test perplexity | 22.7 | #48 of 89 | Archive leaderboard | report |
| Language Modelling | enwiki8 | PAR Transformer 24B | Bit per Character (BPC) | 1.11 | #1 of 1 | Archive leaderboard | report |
| Sentiment Analysis | SST-2 Binary classification | PAR BERT Base | Accuracy | 91.6 | #50 of 87 | 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: PAR Transformer
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