Checkboxes are critical in real-world document processing where the presence or absence of ticks directly informs data extraction and decision-making processes. Yet, despite the strong performance of Large Vision and Language Models across a wide range of tasks, they struggle with interpreting checkable content. This challenge becomes particularly pressing in industries where a single overlooked checkbox may lead to costly regulatory or contractual oversights. To address this gap, we introduce the CheckboxQA dataset, a targeted resource designed to evaluate and improve model performance on checkbox-related tasks. It reveals the limitations of current models and serves as a valuable tool for advancing document comprehension systems, with significant implications for applications in sectors such as legal tech and finance.The dataset is publicly available at:this https URL
View on arXiv@article{turski2025_2504.10419,
title={ Unchecked and Overlooked: Addressing the Checkbox Blind Spot in Large Language Models with CheckboxQA },
author={ Michał Turski and Mateusz Chiliński and Łukasz Borchmann },
journal={arXiv preprint arXiv:2504.10419},
year={ 2025 }
}