@inproceedings{borchmann-etal-2025-arctic,
    title = "Arctic-{TILT}. Business Document Understanding at Sub-Billion Scale",
    author = "Borchmann, {\L}ukasz  and
      Pietruszka, Micha{\l}  and
      Ja{\'s}kowski, Wojciech  and
      Jurkiewicz, Dawid  and
      Halama, Piotr  and
      J{\'o}ziak, Pawe{\l}  and
      Garncarek, {\L}ukasz  and
      Liskowski, Pawe{\l}  and
      Szyndler, Karolina  and
      Gretkowski, Andrzej  and
      O{\l}tusek, Julita  and
      Nowakowska, Gabriela  and
      Zaw{\l}ocki, Artur  and
      Duhr, {\L}ukasz  and
      Dyda, Pawe{\l}  and
      Turski, Micha{\l}",
    editor = "Rehm, Georg  and
      Li, Yunyao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.acl-industry.20/",
    pages = "264--283",
    ISBN = "979-8-89176-288-6",
    abstract = "The vast portion of workloads employing LLMs involves answering questions grounded on PDF or scanned content. We introduce the Arctic-TILT achieving accuracy on par with models 1000$\times$ its size on these use cases. It can be finetuned and deployed on a single 24GB GPU, lowering operational costs while processing 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, essential for processing files in large-scale or time-sensitive enterprise environments. We release Arctic-TILT weights and an efficient vLLM-based implementation on a permissive license."
}