Unchecked and Overlooked: Addressing the Checkbox Blind Spot in Large Language Models with CheckboxQA
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909579905859584 |
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| author | Turski, Michał Chiliński, Mateusz Borchmann, Łukasz |
| author_facet | Turski, Michał Chiliński, Mateusz Borchmann, Łukasz |
| contents | 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: https://github.com/Snowflake-Labs/CheckboxQA |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10419 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unchecked and Overlooked: Addressing the Checkbox Blind Spot in Large Language Models with CheckboxQA Turski, Michał Chiliński, Mateusz Borchmann, Łukasz Computation and Language 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: https://github.com/Snowflake-Labs/CheckboxQA |
| title | Unchecked and Overlooked: Addressing the Checkbox Blind Spot in Large Language Models with CheckboxQA |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2504.10419 |