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Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation

Paper · 2026 · Wei Zhou, Bolei Ma, Annemarie Friedrich et al.

Summary

LLMs and multimodal LLMs have pushed table question answering (TQA) forward quickly. Yet the field still has no unified account of how tasks are defined, what makes them hard, and where methods are heading, including newer threads like reinforcement learning. Zhou, Ma, Friedrich and Mesgar address this with a survey of 277 papers, most from 2022 onward. It covers TQA, table fact verification (TFV), and relevant Text-to-SQL datasets.

The authors describe task setups along five dimensions: domain, table characteristics, additional context, question complexity and answer format. They use these dimensions to classify existing benchmarks. Modeling approaches are grouped by the challenge they target, with a discussion of the pros and cons of each group. The survey also reviews evaluation methodology.

The paper closes with open directions:

  • reinforcement learning with verifiable rewards
  • realistic multimodal and open-domain scenarios
  • explanations that faithfully reflect model reasoning
  • human-centric, socially aware applications

Its main contribution is a single structured reference for a fragmented, fast-moving research area.

Key points

  • Coverage and scope: 277 papers gathered through April 2026 via search queries written in English, drawn largely from arXiv and the venues ACL, EMNLP, NAACL, NeurIPS and ICLR. Table generation and summarization are excluded.

  • Task taxonomy:

    • textual vs. image tables
    • flat vs. hierarchical tables
    • single vs. multiple tables
    • retrieval vs. reasoning questions
    • short-span vs. free-form answers
    • extra inputs (passages, images, charts, knowledge graphs)
    • open vs. closed domain

    Table-text QA is the most widely studied mixed-modality setting.

  • Method taxonomy by challenge:

    • Table Understanding (visual and textual)
    • Complex Query (tuning-based and tuning-free)
    • Large Inputs (large tables, multiple tables)
    • Data Heterogeneity
    • Knowledge Integration

  • RLVR vs. SFT: RLVR-trained models generalize better than SFT models and are more robust to row and column perturbations. However, starting RLVR from an SFT-initialized model remains important for strong results.

  • Interpretability probing:

    • Zhang et al. (2026b) find that cell retrieval proceeds in three stages: semantic binding, coordinate localization (done by counting delimiters) and information extraction.
    • Wang et al. (2026b) report that early layers encode table structure, middle layers locate relevant cells, and later layers produce the answer.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Ludwig-Maximilians-Universität MünchenMunich · Germany107 works · 56,478 citations🎓Munich Center for Machine LearningMunich · Germany57 works · 1,094 citations🏢Robert BoschStuttgart · Germany11 works · 2,306 citations🎓University of AugsburgAugsburg · Germany8 works · 38,342 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksQuestion answering · 441 works

Cite

Cite as Wei Zhou, Bolei Ma, Annemarie Friedrich and Mohsen Mesgar. 2026. Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.557

@inproceedings{zhou2026table,
  title = {{Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation}},
  author = {Wei Zhou, Bolei Ma, Annemarie Friedrich and Mohsen Mesgar},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.557},
  url = {https://doi.org/10.18653/v1/2026.acl-long.557}
}

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