mlsanityData to 7 Oct 2026

MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering

arXiv 2305.12820 ↗First posted 22 May 2023cs.CL cs.AIcited by 32

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Abstract (arXiv)

Recent advances in tabular question answering (QA) with large language models are constrained in their coverage and only answer questions over a single table. However, real-world queries are complex in nature, often over multiple tables in a relational database or web page. Single table questions do not involve common table operations such as set operations, Cartesian products (joins), or nested queries. Furthermore, multi-table operations often result in a tabular output, which necessitates table generation capabilities of tabular QA models. To fill this gap, we propose a new task of answering questions over multiple tables. Our model, MultiTabQA, not only answers questions over multiple tables, but also generalizes to generate tabular answers. To enable effective training, we build a pre-training dataset comprising of 132,645 SQL queries and tabular answers. Further, we evaluate the generated tables by introducing table-specific metrics of varying strictness assessing various levels of granularity of the table structure. MultiTabQA outperforms state-of-the-art single table QA models adapted to a multi-table QA setting by finetuning on three datasets: Spider, Atis and GeoQuery.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 30 of its 41 references that are in the map. First posted on arXiv 22 May 2023.

Builds on

30 of 41 references.

References · 30

  1. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  2. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  3. 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 509
  4. 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
  5. 2021TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Financecited by 402
  6. 2020TaBERT: Pretraining for Joint Understanding of Textual and Tabular Datacited by 395
  7. 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 233
  8. 2021FeTaQA: Free-form Table Question Answeringcited by 174
  9. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  10. 2021Investigating the Limitations of Transformers with Simple Arithmetic Taskscited by 136
  11. 2021HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generationcited by 117
  12. 2021KaggleDBQA: Realistic Evaluation of Text-to-SQL Parserscited by 105
18 more
  1. 2017Learning a Neural Semantic Parser from User Feedbackcited by 98
  2. 2020On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queriescited by 59
  3. 2021MATE: Multi-view Attention for Table Transformer Efficiencycited by 54
  4. 2021Text-to-Table: A New Way of Information Extractioncited by 47
  5. 2022A Survey on Table Question Answering: Recent Advancescited by 41
  6. 2022PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-trainingcited by 41
  7. 2021Open Domain Question Answering with A Unified Knowledge Interfacecited by 32
  8. 2022Table-To-Text generation and pre-training with TabT5cited by 20
  9. 2020Summarizing and Exploring Tabular Data in Conversational Searchcited by 19
  10. 2021FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretrainingcited by 19
  11. 2022TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Datacited by 19
  12. 2022STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsingcited by 19
  13. 2021Investigating Numeracy Learning Ability of a Text-to-Text Transfer Modelcited by 13
  14. 2022Table-based Fact Verification with Self-adaptive Mixture of Expertscited by 12
  15. 2022Parameter-Efficient Abstractive Question Answering over Tables or Textcited by 11
  16. 2018A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table Summarizationcited by 5
  17. 2022Enhancing Tabular Reasoning with Pattern Exploiting Trainingcited by 3
  18. 2022Leveraging Data Recasting to Enhance Tabular Reasoningcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 17; 2025: 12; 2026: 3.

32 papers.

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  1. 2024SPIQA: A Dataset for Multimodal Question Answering on Scientific Paperscited by 75
  2. 2024TableRAG: Million-Token Table Understanding with Language Modelscited by 40
  3. 2024VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generationcited by 40
  4. 2024BEAVER: An Enterprise Benchmark for Text-to-SQLcited by 37
  5. 2024Leopard: A Vision Language Model For Text-Rich Multi-Image Taskscited by 18
  6. 2024TQA-Bench: Evaluating LLMs for Multi-Table Question Answeringcited by 17
  7. 2025RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarkingcited by 17
  8. 2024On the Noise Robustness of In-Context Learning for Text Generationcited by 15
  9. 2024AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Frameworkcited by 12
  10. 2025Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challengescited by 10
  11. 2024QFMTS: Generating Query-Focused Summaries over Multi-Table Inputscited by 9
  12. 2025Sparks of Tabular Reasoning via Text2SQL Reinforcement Learningcited by 9
20 more
  1. 2024Table Question Answering for Low-resourced Indic Languagescited by 7
  2. 2025Plugging Schema Graph into Multi-Table QA: A Human-Guided Framework for Reducing LLM Reliancecited by 7
  3. 2025DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collectionscited by 7
  4. 2025Table as a Modality for Large Language Modelscited by 7
  5. 2024TANQ: An open domain dataset of table answered questionscited by 6
  6. 2024Synthetic SQL Column Descriptions and Their Impact on Text-to-SQL Performancecited by 6
  7. 2025CoddLLM: Empowering Large Language Models for Data Analyticscited by 6
  8. 2024Database-Augmented Query Representation for Information Retrievalcited by 5
  9. 2025Columbo: Expanding Abbreviated Column Names for Tabular Data Using Large Language Modelscited by 5
  10. 2025Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluationcited by 4
  11. 2024Generating Tables from the Parametric Knowledge of Language Modelscited by 3
  12. 2024RETQA: A Large-Scale Open-Domain Tabular Question Answering Dataset for Real Estate Sectorcited by 3
  13. 2025Beyond Natural Language Plans: Structure-Aware Planning for Query-Focused Table Summarizationcited by 1
  14. 2025LyS at SemEval 2025 Task 8: Zero-Shot Code Generation for Tabular QAcited by 1
  15. 2026TACO: Task-Aware Column Description Generation Using LLMscited by 1
  16. 2024Rehearsing Answers to Probable Questions with Perspective-Takingcited by 0
  17. 2024Benchmarking Table Comprehension In The Wildcited by 0
  18. 2025UNJOIN: Enhancing Multi-Table Text-to-SQL Generation via Schema Simplificationcited by 0
  19. 2026Synthetic Contrastive Reasoning for Multi-Table Q&Acited by 0
  20. 2026Latent Bridges for Multi-Table Question Answeringcited by 0