Papers by Piotr Nawrot
Hierarchical Transformers Are More Efficient Language Models (2022.findings-naacl)
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Piotr Nawrot, Szymon Tworkowski, Michał Tyrolski, Lukasz Kaiser, Yuhuai Wu, Christian Szegedy, Henryk Michalewski
| Challenge: | Transformers are impressive but inefficient and costly, which limits their applications and accessibility. |
| Approach: | They first use different ways to downsample and upsamplify activations in Transformers to make them hierarchical. |
| Outcome: | The proposed model outperforms Transformers on the ImageNet32 and enwik8 benchmarks. |
The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs (2026.findings-acl)
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| Challenge: | Sparse attention is a promising strategy to extend long-context capabilities in LLMs . but its efficiency–accuracy trade-offs remain unclear due to the lack of comprehensive evaluation . |
| Approach: | They evaluate sparse attention methods across multiple model families and sizes . they find larger sparser models outperform smaller dense ones at equivalent cost . |
| Outcome: | The proposed methods outperform smaller sparse models at equivalent cost and improve the Pareto frontier. |
Efficient Transformers with Dynamic Token Pooling (2023.acl-long)
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| Challenge: | Hourglass Transformers is a computationally efficient model that can be used to reduce the sequence length in the intermediate layers. |
| Approach: | They propose a dynamic-pooling mechanism which predicts segment boundaries in an autoregressive fashion. |
| Outcome: | The proposed model is faster and more accurate than vanilla Transformers and fixed-length pooling within the same computational budget. |