Enhance MultiTableQA dataset card with introduction, comprehensive sample usage, and task category (#1)

- Enhance MultiTableQA dataset card with introduction, comprehensive sample usage, and task category (0fcee3a4784494d8efafd6121e9163375ceec615)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,14 +1,31 @@
 ---
+license: mit
 configs:
 - config_name: table
-  data_files: "wtq_table.jsonl"
+  data_files: wtq_table.jsonl
 - config_name: test_query
-  data_files: "wtq_query.jsonl"
-
-license: mit
+  data_files: wtq_query.jsonl
+task_categories:
+- table-question-answering
 ---
+
+# RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking
+
 📄 [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**.
+
+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.
+
+## MultiTableQA Benchmark Details
+
 For MultiTableQA, we release a comprehensive benchmark, including five different datasets covering table fact-checking, single-hop QA, and multi-hop QA: 
 | Dataset              | Link |
 |-----------------------|------|
@@ -21,14 +38,101 @@ For MultiTableQA, we release a comprehensive benchmark, including five different
 
 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
+
+The following sections provide instructions on how to set up the environment, prepare the MultiTableQA data, run T-RAG retrieval, and perform downstream inference with LLMs. For more detailed information, please refer to the [official GitHub repository](https://github.com/jiaruzouu/T-RAG).
+
+### 1. Installation
+
+To get started with the T-RAG framework, first clone the repository and install the necessary dependencies:
+
+```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
+```
+
+### 2. MultiTableQA Data Preparation
+
+To download and preprocess the **MultiTableQA** benchmark:
+
+```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.
+
+### 3. 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,
+
+```bash
+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,
+```bash
+cd src
+cd table2graph
+python scripts/subgraph_retrieve_run.py
+```
+
+*Note: Our method supports different embedding methods such as E5, contriever, sentence-transformer, etc.*
+
+### 4. 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`
+```json
+{
+    "openai": "<YOUR_OPENAI_API_KEY>",
+    "claude": "<YOUR_CLAUDE_API_KEY>"
+}
+```
+
+To run end-to-end model inference and evaluation,
+
+```bash
+cd src
+cd downstream_inference
+bash scripts/overall_run.sh
+```
+
 ---
 # Citation
 
 If you find our work useful, please cite:
 
 ```bibtex
-@misc{zou2025gtrgraphtableragcrosstablequestion,
-      title={GTR: Graph-Table-RAG for Cross-Table Question Answering}, 
+@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},