--- 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://arxiv.org/abs/2504.01346) | 👨🏻‍💻 [Code](https://github.com/jiaruzouu/T-RAG) ## 🔍 Introduction Retrieval-Augmented Generation (RAG) has become a key paradigm to enhance Large Language Models (LLMs) with external knowledge. While most RAG systems focus on **text corpora**, real-world information is often stored in **tables** across web pages, Wikipedia, and relational databases. Existing methods struggle to retrieve and reason across **multiple heterogeneous tables**. 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. --- # 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}, } ```