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

Source: [BoundingDocs: a Unified Dataset for Document Question Answering with Spatial Annotations](<https://www.sota2.com/research/paper/68fa76781e209131cea6d36a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-13T05:01:26.895Z

Authors: Simone Giovannini, Fabio Coppini, Andrea Gemelli, Simone Marinai

Published: 2025-01-06T21:46:22.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2501.03403>)

[PDF](<https://arxiv.org/pdf/2501.03403.pdf>)