BoundingDocs: a Unified Dataset for Document Question Answering with Spatial Annotations

Simone Giovannini, Fabio Coppini, Andrea Gemelli, Simone Marinai
1/6/2025
cs.CLcs.AI

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Abstract

We present a unified dataset for document Question-Answering (QA), which is obtained combining several public datasets related to Document AI and visually rich document understanding (VRDU). Our main contribution is twofold: on the one hand we reformulate existing Document AI tasks, such as Information Extraction (IE), into a Question-Answering task, making it a suitable resource for training and evaluating Large Language Models; on the other hand, we release the OCR of all the documents and include the exact position of the answer to be found in the document image as a bounding box. Using this dataset, we explore the impact of different prompting techniques (that might include bounding box information) on the performance of open-weight models, identifying the most effective approaches for document comprehension.

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Cite this paper

@article{giovannini2025boundingdocs,
  title  = {BoundingDocs: a Unified Dataset for Document Question Answering with Spatial Annotations},
  author = {Simone Giovannini and Fabio Coppini and Andrea Gemelli and Simone Marinai},
  year   = {2025},
  eprint = {2501.03403},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.03403v3}
}

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