title: Unchecked and Overlooked: Addressing the Checkbox Blind Spot in Large
    Language Models with CheckboxQA
summary: 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
paper_authors._id: 68026f762e2023f6cf7f0daa
paper_authors.name: Michał Turski
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-04-18T15:27:52.927Z
paper_authors._id: 680268a7fd1fae58d58a2b49
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/P96qvBdInKYnVxij7-MM_.png
paper_authors.fullname: Michał Turski
paper_authors.type: user
paper_authors.user: mturski
paper_authors._id: 68026f762e2023f6cf7f0dab
paper_authors.name: Mateusz Chiliński
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-24T11:53:46.684Z
paper_authors._id: 6728d349a30b64dfcb517270
paper_authors.avatarUrl: /avatars/78083b511eedb76ee85f9c92faecdc78.svg
paper_authors.fullname: Mateusz Chilinski
paper_authors.type: user
paper_authors.user: sf-mchilinski
paper_authors._id: 68026f762e2023f6cf7f0dac
paper_authors.name: Łukasz Borchmann
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-24T11:53:52.942Z
paper_authors._id: 600b381d3cc3b87db94bc0ce
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/600b381d3cc3b87db94bc0ce/I3xpr4gzcG1uXawXBpWpD.jpeg
paper_authors.fullname: Łukasz Borchmann
paper_authors.type: user
paper_authors.user: Borchmann
paper_title: Unchecked and Overlooked: Addressing the Checkbox Blind Spot in Large
    Language Models with CheckboxQA
paper_summary: 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
paper_ai_summary: The CheckboxQA dataset evaluates and improves model performance on interpreting checkboxes in document processing, crucial for minimizing errors in industries like legal tech and finance.
paper_ai_keywords: CheckboxQA document comprehension systems