Papers by Łukasz Garncarek
Sparsifying Transformer Models with Trainable Representation Pooling (2022.acl-long)
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| Challenge: | Existing approaches to sparsify attention in the Transformer model are based on quadratic memory complexity and a lack of information for each word. |
| Approach: | They propose a method to sparsify attention in a Transformer model by learning to select the most-informative token representations during the training process. |
| Outcome: | The proposed model performs better than the current SOTA model while being 1.8 faster during training, 4.5 faster inference and 13 more efficient in the decoder. |
Arctic-TILT. Business Document Understanding at Sub-Billion Scale (2025.acl-industry)
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Łukasz Borchmann, Michał Pietruszka, Wojciech Jaśkowski, Dawid Jurkiewicz, Piotr Halama, Paweł Józiak, Łukasz Garncarek, Paweł Liskowski, Karolina Szyndler, Andrzej Gretkowski, Julita Ołtusek, Gabriela Nowakowska, Artur Zawłocki, Łukasz Duhr, Paweł Dyda, Michał Turski
| Challenge: | General-purpose LLMs and their multimodal counterparts provide a crucial advantage in process automation. |
| Approach: | They propose a model that can be finetuned and deployed on a single 24GB GPU . it provides reliable confidence scores and quick inferences for processing files in large-scale or time-sensitive environments. |
| Outcome: | The proposed model achieves state-of-the-art results on seven diverse benchmarks and provides reliable confidence scores and quick inferences. |
STable: Table Generation Framework for Encoder-Decoder Models (2024.eacl-long)
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Michał Pietruszka, Michał Turski, Łukasz Borchmann, Tomasz Dwojak, Gabriela Nowakowska, Karolina Szyndler, Dawid Jurkiewicz, Łukasz Garncarek
| Challenge: | Existing approaches to infer text-to-table neural models are limited to raw text, but the proposed framework is capable of unifying a variety of problems involving natural language. |
| Approach: | They propose a framework for text-to-table neural models that utilizes a generalized sequential method that comprehends information from all cells in the table. |
| Outcome: | The proposed framework outperforms previous approaches on several challenging datasets and outperformed existing models by up to 15%. |