Papers by Szymon Tworkowski
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. |
Analysing The Impact of Sequence Composition on Language Model Pre-Training (2024.acl-long)
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Yu Zhao, Yuanbin Qu, Konrad Staniszewski, Szymon Tworkowski, Wei Liu, Piotr Miłoś, Yuxiang Wu, Pasquale Minervini
| Challenge: | Existing studies show that pretraining sequence composition strategy can lead to distracting information from previous documents. |
| Approach: | They propose to use a sequence construction method to concatenate documents into fixed-length sequences to compute the likelihood of each token given its context. |
| Outcome: | The proposed method can improve in-context learning, knowledge memorisation and context utilisation without sacrificing efficiency. |