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README.md

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+---
+license: mit
+---
+
+# pdfQA: Diverse, Challenging, and Realistic Question Answering over PDFs
+
+[pdfQA](https://arxiv.org/abs/2601.02285) is a structured benchmark collection for document-level question answering and PDF understanding research.
+
+The dataset is organized to support:
+
+* Raw document processing research
+* Structured extraction pipelines
+* Retrieval-augmented QA
+* End-to-end document reasoning systems
+
+It preserves original documents alongside structured derivatives to enable reproducible evaluation across preprocessing strategies.
+
+---
+
+## Dataset Structure
+
+The repository follows a strict hierarchical layout:
+
+```
+<category>/<type>/<dataset>/...
+```
+
+### Categories
+
+* `real-pdfQA/` — Real-world benchmark datasets
+* `syn-pdfQA/` — Synthetic benchmark datasets
+
+### Types
+
+Each dataset contains three file-type folders:
+
+* `01.1_Input_Files_Non_PDF/` — Original source formats (e.g., xlsx, epub, htm, tex, txt)
+* `01.2_Input_Files_PDF/` — Original PDF files
+* `01.3_Input_Files_CSV/` — Structured annotations / tabular representations
+
+### Datasets
+Each type folder contains subfolders for individual datasets. Supported datasets include:
+
+#### Real-world Datasets
+- `ClimateFinanceBench/`
+- `ClimRetrieve/`
+- `FeTaQA/`
+- `FinanceBench/`
+- `FinQA/`
+- `NaturalQuestions/`
+- `PaperTab/`
+- `PaperText/`
+- `Tat-QA/`
+
+#### Synthetic Datasets
+- `books/`
+- `financial_reports/`
+- `sustainability_disclosures/`
+- `research_articles/`
+
+
+### Example
+
+```
+syn-pdfQA/
+  01.2_Input_Files_PDF/
+    books/
+      file1.pdf
+  01.3_Input_Files_CSV/
+    books/
+      file1.csv
+  01.1_Input_Files_Non_PDF/
+    books/
+      file1.xlsx
+```
+
+This design allows:
+
+* Access to original PDFs
+* Access to structured evaluation data
+* Access to original source formats for preprocessing research
+
+---
+
+## Intended Use
+
+This dataset is intended for:
+
+* PDF parsing and layout understanding
+* Financial and sustainability document QA
+* Retrieval-augmented generation (RAG)
+* Multi-modal document pipelines
+* Table extraction and structured reasoning
+* Robustness evaluation across preprocessing pipelines
+
+It is particularly useful for comparing:
+
+* Direct PDF-based reasoning
+* OCR pipelines
+* Structured table extraction
+* Raw-source ingestion approaches
+
+---
+
+## Access Patterns
+
+The dataset can be accessed in multiple ways depending on research needs. Scripts for downloading specific subsets are provided on [Github](https://github.com/tobischimanski/pdfQA).
+
+The repository supports multiple usage patterns:
+
+#### Download Everything
+
+Clone the entire repository (includes Git LFS objects).
+
+#### Download by Category
+
+Download only `real-pdfQA/` or only `syn-pdfQA/`.
+
+#### Download by Dataset
+
+Download a specific dataset across all file types.
+
+#### Download by Dataset + Type
+
+Example:
+
+* Only PDFs for a dataset
+* Only CSV annotations for a dataset
+* Only raw source files for a dataset
+
+#### Download Single File
+
+Individual files can be downloaded directly via the Hugging Face API.
+
+Example (Python):
+
+```python
+from huggingface_hub import hf_hub_download
+
+hf_hub_download(
+    repo_id="pdfqa/pdfQA",
+    repo_type="dataset",
+    filename="syn-pdfQA/01.2_Input_Files_PDF/FinQA/AAL_2010.pdf"
+)
+```
+
+---
+
+## Data Modalities
+
+Depending on the dataset:
+
+* Financial reports
+* Sustainability disclosures
+* Structured financial QA corpora
+* Table-heavy documents
+* Mixed structured/unstructured content
+
+Formats may include:
+
+* PDF
+* CSV
+* XLS/XLSX
+* EPUB
+* HTML/HTM
+* TEX
+* TXT
+
+---
+
+## Research Motivation
+
+Many document QA benchmarks release only structured data or only PDFs.
+pdfQA preserves **all representations**:
+
+* Original document
+* Structured derivative
+* Raw source format (if available)
+
+This enables:
+
+* Studying preprocessing impact
+* Comparing parsing strategies
+* Evaluating robustness to format variation
+* End-to-end pipeline benchmarking
+
+---
+
+## Limitations
+
+* Some datasets originate from external benchmarks and inherit their constraints.
+* Synthetic datasets may not reflect real-world noise distribution.
+* File sizes can be large due to preservation of raw formats.
+* Users must install Git LFS for full cloning.
+
+---
+
+## License
+
+Each sub-dataset inherits the license of its original source.
+Users must ensure compliance with the licensing terms of individual datasets.
+
+If you use this collection, please cite the original datasets in addition to this repository.
+
+---
+
+## Citation
+
+If you use **pdfQA**, please cite:
+
+```
+@misc{schimanski2026pdfqa,
+      title={pdfQA: Diverse, Challenging, and Realistic Question Answering over PDFs}, 
+      author={Tobias Schimanski and Imene Kolli and Yu Fan and Ario Saeid Vaghefi and Jingwei Ni and Elliott Ash and Markus Leippold},
+      year={2026},
+      eprint={2601.02285},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2601.02285}, 
+}
+```
+
+---
+
+## Contact
+
+Visit [https://github.com/tobischimanski/pdfQA](https://github.com/tobischimanski/pdfQA) for access and updates.