README.md

---
license: mit
task_categories:
- question-answering
- table-question-answering
language:
- en
tags:
- research
- climate
- finance
- research
- evaluation
- reasoning
---

# 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 QA annotations are released as a separate Hugging Face dataset:  
> https://huggingface.co/datasets/pdfqa/pdfQA-Annotations  
> (Dataset ID: `pdfqa/pdfQA-Annotations`)

This 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 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 supports multiple access patterns depending on research
needs.

All official download scripts are available in the GitHub repository:

👉 https://github.com/tobischimanski/pdfQA

Scripts are provided in both:

-   **Bash (git + Git LFS)** --- recommended for large-scale downloads\
-   **Python (huggingface_hub API)** --- recommended for programmatic
    workflows

------------------------------------------------------------------------

### 1️⃣ Download Everything

Download the entire repository (all categories, types, and datasets).

#### Bash (git + LFS)

``` bash
./tools/download_using_bash/download_all.sh
```

[Bash script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_bash/download_all.sh)


#### Python (HF API)

``` bash
python tools/download_using_python/download_all.py
```

[Python script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_python/download_all.py)

------------------------------------------------------------------------

### 2️⃣ Download by Category

Download only:

-   `real-pdfQA/`
-   or `syn-pdfQA/`

#### Example

``` bash
./tools/download_using_bash/download_category.sh syn-pdfQA
```

[Bash script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_bash/download_category.sh)

[Python script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_python/download_category.py)

------------------------------------------------------------------------

### 3️⃣ Download by Dataset (All Types)

Download a single dataset across all three file-type folders:

-   `01.1_Input_Files_Non_PDF/`
-   `01.2_Input_Files_PDF/`
-   `01.3_Input_Files_CSV/`

#### Example

``` bash
./tools/download_using_bash/download_dataset.sh syn-pdfQA books
```

[Bash script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_bash/download_dataset.sh)

[Python script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_python/download_dataset.py)

------------------------------------------------------------------------

### 4️⃣ Download Arbitrary Folders

Download one or multiple arbitrary folder paths.

#### Example

``` bash
./tools/download_using_bash/download_folders.sh \
  "syn-pdfQA/01.2_Input_Files_PDF/books" \
  "syn-pdfQA/01.3_Input_Files_CSV/books"
```

[Bash script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_bash/download_folders.sh)

[Python script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_python/download_folders.py)

------------------------------------------------------------------------

### 5️⃣ Download Specific Files

Download one or more individual files.

#### Example (Bash)

``` bash
./tools/download_using_bash/download_files.sh \
  "syn-pdfQA/01.2_Input_Files_PDF/books/file1.pdf"
```

[Bash script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_bash/download_files.sh)

[Python script](https://github.com/tobischimanski/pdfQA/blob/main/tools/download_using_python/download_files.py)

------------------------------------------------------------------------

### 6️⃣ Direct API Access (Single File)

Files can also be downloaded directly using the Hugging Face API. Example:

``` python
from huggingface_hub import hf_hub_download

hf_hub_download(
    repo_id="pdfqa/pdfQA-Benchmark",
    repo_type="dataset",
    filename="syn-pdfQA/01.2_Input_Files_PDF/books/978-3-030-10752-9.pdf"
)
```

------------------------------------------------------------------------

## Recommended Usage

-   For **large-scale research experiments** → use **Bash + git LFS**
    (fully resumable).
-   For **automated pipelines** → use **Python scripts**.
-   For **fine-grained subset control** → use folder or file-based
    scripts.

---

## 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

---

## 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.