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.
@article{arxiv.2501.03403,
title = {BoundingDocs: a Unified Dataset for Document Question Answering with Spatial Annotations},
author = {Simone Giovannini and Fabio Coppini and Andrea Gemelli and Simone Marinai},
journal= {arXiv preprint arXiv:2501.03403},
year = {2025}
}