# pdfQA: Diverse, Challenging, and Realistic Question Answering over PDFs

Source: [pdfQA: Diverse, Challenging, and Realistic Question Answering over PDFs](<https://www.sota2.com/research/paper/695c970653fdd5c3617437a5>)

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

Data updated: 2026-01-08T05:00:05.049Z

Authors: Tobias Schimanski, Imene Kolli, Yu Fan, Ario Saeid Vaghefi, Jingwei Ni, Elliott Ash, Markus Leippold

Published: 2026-01-05T17:15:26.000Z

## Abstract

PDFs are the second-most used document type on the internet (after HTML). Yet, existing QA datasets commonly start from text sources or only address specific domains. In this paper, we present pdfQA, a multi-domain 2K human-annotated (real-pdfQA) and 2K synthetic dataset (syn-pdfQA) differentiating QA pairs in ten complexity dimensions (e.g., file type, source modality, source position, answer type). We apply and evaluate quality and difficulty filters on both datasets, obtaining valid and challenging QA pairs. We answer the questions with open-source LLMs, revealing existing challenges that correlate with our complexity dimensions. pdfQA presents a basis for end-to-end QA pipeline evaluation, testing diverse skill sets and local optimizations (e.g., in information retrieval or parsing).

## 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/2601.02285>)

[PDF](<https://arxiv.org/pdf/2601.02285.pdf>)