mlsanityData to 7 Oct 2026

SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA

arXiv 2409.16682 ↗First posted 25 Sep 2024cs.CLcited by 9

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Abstract (arXiv)

Text-to-SQL parsing and end-to-end question answering (E2E TQA) are two main approaches for Table-based Question Answering task. Despite success on multiple benchmarks, they have yet to be compared and their synergy remains unexplored. In this paper, we identify different strengths and weaknesses through evaluating state-of-the-art models on benchmark datasets: Text-to-SQL demonstrates superiority in handling questions involving arithmetic operations and long tables; E2E TQA excels in addressing ambiguous questions, non-standard table schema, and complex table contents. To combine both strengths, we propose a Synergistic Table-based Question Answering approach that integrate different models via answer selection, which is agnostic to any model types. Further experiments validate that ensembling models by either feature-based or LLM-based answer selector significantly improves the performance over individual models.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 35 of its 45 references that are in the map. First posted on arXiv 25 Sep 2024.

Builds on

35 of 45 references.

References · 35

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  3. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  4. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  5. 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 509
  6. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 465
  7. 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
  8. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  9. 2021TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Financecited by 402
  10. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
  11. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 378
  12. 2020HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Datacited by 263
23 more
  1. 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 233
  2. 2022Binding Language Models in Symbolic Languagescited by 224
  3. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  4. 2022Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Modelscited by 187
  5. 2023Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoningcited by 177
  6. 2019SParC: Cross-Domain Semantic Parsing in Contextcited by 141
  7. 2020Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsingcited by 133
  8. 2023Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sourcescited by 121
  9. 2022Large Language Models are few(1)-shot Table Reasonerscited by 116
  10. 2020SmBoP: Semi-autoregressive Bottom-up Semantic Parsingcited by 97
  11. 2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
  12. 2021Open Domain Question Answering over Tables via Dense Retrievalcited by 74
  13. 2023Table-GPT: Table-tuned GPT for Diverse Table Taskscited by 69
  14. 2020Understanding tables with intermediate pre-trainingcited by 68
  15. 2020On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queriescited by 59
  16. 2023Scaling Expert Language Models with Unsupervised Domain Discoverycited by 59
  17. 2023Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMscited by 49
  18. 2023Getting MoRE out of Mixture of Language Model Reasoning Expertscited by 44
  19. 2023RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbationscited by 41
  20. 2022ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examplescited by 29
  21. 2022Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbationcited by 28
  22. 2023TempTabQA: Temporal Question Answering for Semi-Structured Tablescited by 27
  23. 2021MetaQA: Combining Expert Agents for Multi-Skill Question Answeringcited by 15

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 7; 2026: 1.

9 papers.

  1. 2024MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answeringcited by 17
  2. 2025RePanda: Pandas-powered Tabular Verification and Reasoningcited by 5
  3. 2025Mixture of Length and Pruning Experts for Knowledge Graphs Reasoningcited by 5
  4. 2025Improved LLM Agents for Financial Document Question Answeringcited by 4
  5. 2025Chain-of-Query: Unleashing the Power of LLMs in SQL-Aided Table Understanding via Multi-Agent Collaborationcited by 4
  6. 2025Same Content, Different Representations: A Controlled Study for Table QAcited by 3
  7. 2025Towards Question Answering over Large Semi-structured Tablescited by 2
  8. 2025ITUNLP at SemEval-2025 Task 8: Question-Answering over Tabular Data: A Zero-Shot Approach using LLM-Driven Code Generationcited by 1
  9. 2026MATA: Multi-Agent Framework for Reliable and Flexible Table Question Answeringcited by 1