---
license: mit
task_categories:
- table-question-answering
configs:
- config_name: table
  data_files: sqa_table.jsonl
- config_name: test_query
  data_files: sqa_query.jsonl
---

📄 [Paper](https://huggingface.co/papers/2504.01346) | 👨🏻‍💻 [Code](https://github.com/jiaruzouu/T-RAG)

For MultiTableQA, we release a comprehensive benchmark, including five different datasets covering table fact-checking, single-hop QA, and multi-hop QA: 
| Dataset              | Link |
|-----------------------|------|
| MultiTableQA-TATQA    | 🤗 [dataset link](https://huggingface.co/datasets/jiaruz2/MultiTableQA_TATQA)   |
| MultiTableQA-TabFact  | 🤗 [dataset link](https://huggingface.co/datasets/jiaruz2/MultiTableQA_TabFact) |
| MultiTableQA-SQA      | 🤗 [dataset link](https://huggingface.co/datasets/jiaruz2/MultiTableQA_SQA)     |
| MultiTableQA-WTQ      | 🤗 [dataset link](https://huggingface.co/datasets/jiaruz2/MultiTableQA_WTQ)     |
| MultiTableQA-HybridQA | 🤗 [dataset link](https://huggingface.co/datasets/jiaruz2/MultiTableQA_HybridQA)|


MultiTableQA extends the traditional single-table QA setting into a multi-table retrieval and question answering benchmark, enabling more realistic and challenging evaluations.

## Sample Usage

This dataset (`MultiTableQA-SQA`) is part of the larger **MultiTableQA** benchmark. To prepare the full benchmark, you can follow these steps from the official T-RAG GitHub repository.

First, clone the repository and set up the environment:

```bash
git clone https://github.com/jiaruzouu/T-RAG.git
cd T-RAG

conda create -n trag python=3.11.9
conda activate trag

# Install dependencies
pip install -r requirements.txt
```

Then, navigate to the `table2graph` directory and run the data preparation script:

```bash
cd table2graph
bash scripts/prepare_data.sh
```

This script will automatically fetch the source tables, apply decomposition (row/column splitting), and generate the benchmark splits, including the `MultiTableQA-SQA` data available in this repository.

---
# Citation

If you find our work useful, please cite:

```bibtex
@misc{zou2025rag,
      title={RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking}, 
      author={Jiaru Zou and Dongqi Fu and Sirui Chen and Xinrui He and Zihao Li and Yada Zhu and Jiawei Han and Jingrui He},
      year={2025},
      eprint={2504.01346},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2504.01346}, 
}
```