# TableQA: Question Answering on Tabular Data

arXiv: https://arxiv.org/abs/1705.06504 · HTML: https://mlsanity.com/paper/1705.06504/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2017.

## Built on by (most cited first, 8 of 8)

- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers](https://mlsanity.com/paper/2407.09413/) (2024)
- [RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking](https://mlsanity.com/paper/2504.01346/) (2025)
- [Table Structure Recognition with Conditional Attention](https://mlsanity.com/paper/2203.03819/) (2022)
- [TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning](https://mlsanity.com/paper/2510.06217/) (2025)
- [Large Language Models Understand Layout](https://mlsanity.com/paper/2407.05750/) (2024)
- [Embeddings for Tabular Data: A Survey](https://mlsanity.com/paper/2302.11777/) (2023)
- [Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification](https://mlsanity.com/paper/2508.21561/) (2025)

## Abstract (arXiv)

Tabular data is difficult to analyze and to search through, yielding for new tools and interfaces that would allow even non tech-savvy users to gain insights from open datasets without resorting to specialized data analysis tools or even without having to fully understand the dataset structure. The goal of our demonstration is to showcase answering natural language questions from tabular data, and to discuss related system configuration and model training aspects. Our prototype is publicly available and open-sourced (see https://svakulenko.ai.wu.ac.at/tableqa).