Datasets:

jiaruz2
/

MultiTableQA_TATQA

license: mit
configs:
  - config_name: table
    data_files: tatqa_table.jsonl
  - config_name: test_query
    data_files: tatqa_query.jsonl
task_categories:
  - table-question-answering

📄 Paper | 👨🏻‍💻 Code

🔍 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.

This repository provides the implementation of T-RAG, a novel table-corpora-aware RAG framework featuring:

Hierarchical Memory Index – organizes heterogeneous table knowledge at multiple granularities.
Multi-Stage Retrieval – coarse-to-fine retrieval combining clustering, subgraph reasoning, and PageRank.
Graph-Aware Prompting – injects relational priors into LLMs for structured tabular reasoning.
MultiTableQA Benchmark – a large-scale dataset with 57,193 tables and 23,758 questions across various tabular tasks.

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
MultiTableQA-TabFact	🤗 dataset link
MultiTableQA-SQA	🤗 dataset link
MultiTableQA-WTQ	🤗 dataset link
MultiTableQA-HybridQA	🤗 dataset link

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

To get started with the T-RAG framework and the MultiTableQA benchmark, follow these steps.

🚀 Installation

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

1. MultiTableQA Data Preparation

To download and preprocess the MultiTableQA benchmark:

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.

2. Run T-RAG Retrieval

To run hierarchical index construction and multi-stage retrieval:

Stage 1 & 2: Table to Graph Construction & Coarse-grained Multi-way Retrieval

Stages 1 & 2 include:

Table Linearization
Multi-way Feature Extraction
Hypergraph Construction by Multi-way Clustering
Typical Node Selection for Efficient Table Retrieval
Query-Cluster Assignment

To run this,

cd src
cd table2graph
bash scripts/table_cluster_run.sh #  or python scripts/table_cluster_run.py

Stage 3: Fine-grained sub-graph Retrieval Stage 3 includes:

Local Subgraph Construction
Iterative Personalized PageRank for Retrieval.

To run this,

cd src
cd table2graph
python scripts/subgraph_retrieve_run.py

Note: Our method supports different embedding methods such as E5, contriever, sentence-transformer, etc.*

3. Downstream Inference with LLMs

Evaluate T-RAG with an (open/closed-source) LLM of your choice (e.g., GPT-4o, Claude-3.5, Qwen):

For Closed-source LLM, please first insert your key under key.json

{
    "openai": "<YOUR_OPENAI_API_KEY>",
    "claude": "<YOUR_CLAUDE_API_KEY>"
}

To run end-to-end model inference and evaluation,

cd src
cd downstream_inference
bash scripts/overall_run.sh

Citation

If you find our work useful, please cite:

@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}, 
}