# TabMCQ: A Dataset of General Knowledge Tables and Multiple-choice Questions

arXiv: https://arxiv.org/abs/1602.03960 · HTML: https://mlsanity.com/paper/1602.03960/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2016.

## Built on by (most cited first, 18 of 18)

- [Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning](https://mlsanity.com/paper/2209.14610/) (2022)
- [STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases](https://mlsanity.com/paper/2404.13207/) (2024)
- [AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry](https://mlsanity.com/paper/2106.12944/) (2021)
- [Multimodal Table Understanding](https://mlsanity.com/paper/2406.08100/) (2024)
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- [Capturing Row and Column Semantics in Transformer Based Question Answering over Tables](https://mlsanity.com/paper/2104.08303/) (2021)
- [Table-R1: Inference-Time Scaling for Table Reasoning](https://mlsanity.com/paper/2505.23621/) (2025)
- [EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain](https://mlsanity.com/paper/2210.06104/) (2022)
- [CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering](https://mlsanity.com/paper/2106.04441/) (2021)
- [Evaluating the Knowledge Dependency of Questions](https://mlsanity.com/paper/2211.11902/) (2022)
- [Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training Framework](https://mlsanity.com/paper/2212.10097/) (2022)
- [TABLET: A Large-Scale Dataset for Robust Visual Table Understanding](https://mlsanity.com/paper/2509.21205/) (2025)
- [CT2C-QA: Multimodal Question Answering over Chinese Text, Table and Chart](https://mlsanity.com/paper/2410.21414/) (2024)
- [TableDreamer: Progressive and Weakness-guided Data Synthesis from Scratch for Table Instruction Tuning](https://mlsanity.com/paper/2506.08646/) (2025)
- [TabFlash: Efficient Table Understanding with Progressive Question Conditioning and Token Focusing](https://mlsanity.com/paper/2511.13283/) (2025)
- [Decoupling Skeleton and Flesh: Efficient Multimodal Table Reasoning with Disentangled Alignment and Structure-aware Guidance](https://mlsanity.com/paper/2602.03491/) (2026)
- [Efficient Table Retrieval and Understanding with Multimodal Large Language Models](https://mlsanity.com/paper/2602.07642/) (2026)
- [What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects](https://mlsanity.com/paper/2501.14717/) (2025)

## Abstract (arXiv)

We describe two new related resources that facilitate modelling of general knowledge reasoning in 4th grade science exams. The first is a collection of curated facts in the form of tables, and the second is a large set of crowd-sourced multiple-choice questions covering the facts in the tables. Through the setup of the crowd-sourced annotation task we obtain implicit alignment information between questions and tables. We envisage that the resources will be useful not only to researchers working on question answering, but also to people investigating a diverse range of other applications such as information extraction, question parsing, answer type identification, and lexical semantic modelling.