4 citations · 7 across the 10 of their papers we have counts for
9 papers · 1 filter
Can Models Help Us Create Better Models? Evaluating LLMs as Data Scientists
Michał Pietruszka, Łukasz Borchmann, Aleksander Jędrosz +1
We present a benchmark for large language models designed to tackle one of the most knowledge-intensive tasks in data science: writing feature engineering code, which requires doma…
Arctic-TILT. Business Document Understanding at Sub-Billion Scale
Łukasz Borchmann, Michał Pietruszka, Wojciech Jaśkowski +13
The vast portion of workloads employing LLMs involves answering questions grounded on PDF or scan content. We introduce the Arctic-TILT achieving accuracy on par with models 1000$\…
STable: Table Generation Framework for Encoder-Decoder Models
Michał Pietruszka, Michał Turski, Łukasz Borchmann +5
The output structure of database-like tables, consisting of values structured in horizontal rows and vertical columns identifiable by name, can cover a wide range of NLP tasks. Fol…
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer
Rafał Powalski, Łukasz Borchmann, Dawid Jurkiewicz +3
We address the challenging problem of Natural Language Comprehension beyond plain-text documents by introducing the TILT neural network architecture which simultaneously learns lay…
From Dataset Recycling to Multi-Property Extraction and Beyond
Tomasz Dwojak, Michał Pietruszka, Łukasz Borchmann +2
This paper investigates various Transformer architectures on the WikiReading Information Extraction and Machine Reading Comprehension dataset. The proposed dual-source model outper…
Sparsifying Transformer Models with Trainable Representation Pooling
Michał Pietruszka, Łukasz Borchmann, Łukasz Garncarek
We propose a novel method to sparsify attention in the Transformer model by learning to select the most-informative token representations during the training process, thus focusing…
On the Multi-Property Extraction and Beyond
Tomasz Dwojak, Michał Pietruszka, Łukasz Borchmann +2
In this paper, we investigate the Dual-source Transformer architecture on the WikiReading information extraction and machine reading comprehension dataset. The proposed model outpe…
ApplicaAI at SemEval-2020 Task 11: On RoBERTa-CRF, Span CLS and Whether Self-Training Helps Them
Dawid Jurkiewicz, Łukasz Borchmann, Izabela Kosmala +1
This paper presents the winning system for the propaganda Technique Classification (TC) task and the second-placed system for the propaganda Span Identification (SI) task. The purp…
Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines
Łukasz Borchmann, Dawid Wiśniewski, Andrzej Gretkowski +7
We propose a new shared task of semantic retrieval from legal texts, in which a so-called contract discovery is to be performed, where legal clauses are extracted from documents, g…