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Arctic-TILT. Business Document Understanding at Sub-Billion Scale

Łukasz Borchmann
Michał Pietruszka
Wojciech Ja'skowski
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
Abstract

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×\times its size on these use cases. It can be fine-tuned and deployed on a single 24GB GPU, lowering operational costs while processing Visually Rich Documents with up to 400k tokens. The model establishes state-of-the-art results on seven diverse Document Understanding benchmarks, as well as provides reliable confidence scores and quick inference, which are essential for processing files in large-scale or time-sensitive enterprise environments.

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