Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2408.04632 v1 pith:UVBPVBXV submitted 2024-08-08 cs.CL cs.CV

{L}ukasz Borchmann , Micha{l} Pietruszka , Wojciech Jaśkowski , Dawid Jurkiewicz , Piotr Halama , Pawe{l} Józiak , {L}ukasz Garncarek , Pawe{l} Liskowski
show 8 more authors
Karolina Szyndler Andrzej Gretkowski Julita O{l}tusek Gabriela Nowakowska Artur Zaw{l}ocki {L}ukasz Duhr Pawe{l} Dyda Micha{l} Turski
This is my paper · ORCID
classification cs.CLcs.CV
keywords arctic-tiltdocumentprocessingunderstandingaccuracyachievingansweringbenchmarks
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends

    cs.CL 2025-01 conditional novelty 3.0 of 10

    A structured overview of question answering over visually rich documents, comparing encoding, vision-only, and multi-page methods, and highlighting comparability issues in existing benchmarks.

Pith tools