mlsanityData to 7 Oct 2026

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

arXiv 1602.03960 ↗First posted 12 Feb 2016cs.CLcited by 18

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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.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Feb 2016.

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Citing papers per year: 2021: 3; 2022: 5; 2023: 0; 2024: 3; 2025: 5; 2026: 2.

18 papers.

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  1. 2022Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoningcited by 347
  2. 2024STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Basescited by 53
  3. 2021AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industrycited by 44
  4. 2024Multimodal Table Understandingcited by 43
  5. 2022A Survey on Table Question Answering: Recent Advancescited by 41
  6. 2021Capturing Row and Column Semantics in Transformer Based Question Answering over Tablescited by 31
  7. 2025Table-R1: Inference-Time Scaling for Table Reasoningcited by 23
  8. 2022EduQG: A Multi-format Multiple Choice Dataset for the Educational Domaincited by 11
  9. 2021CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answeringcited by 9
  10. 2022Evaluating the Knowledge Dependency of Questionscited by 9
  11. 2022Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training Frameworkcited by 6
  12. 2025TABLET: A Large-Scale Dataset for Robust Visual Table Understandingcited by 5
6 more
  1. 2024CT2C-QA: Multimodal Question Answering over Chinese Text, Table and Chartcited by 4
  2. 2025TableDreamer: Progressive and Weakness-guided Data Synthesis from Scratch for Table Instruction Tuningcited by 4
  3. 2025TabFlash: Efficient Table Understanding with Progressive Question Conditioning and Token Focusingcited by 3
  4. 2026Decoupling Skeleton and Flesh: Efficient Multimodal Table Reasoning with Disentangled Alignment and Structure-aware Guidancecited by 3
  5. 2025What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effectscited by 2
  6. 2026Efficient Table Retrieval and Understanding with Multimodal Large Language Modelscited by 2