@inproceedings{zhou-etal-2026-table,
title = "Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation",
author = "Zhou, Wei and
Ma, Bolei and
Friedrich, Annemarie and
Mesgar, Mohsen",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.557/",
pages = "12161--12189",
ISBN = "979-8-89176-390-6",
abstract = "Table Question Answering (TQA) aims to answer natural language questions using tabular data, often accompanied by additional contexts, such as passages. The task spans diverse settings, varying in table representation, question/answer complexity, modality involved, and domain. While recent advances in large language models (LLMs) have driven substantial progress for TQA, the field still lacks a systematic organization and understanding of task formulations, core challenges, and methodological trends, particularly in light of emerging research directions, such as reinforcement learning. This survey addresses this gap by providing a comprehensive and structured overview of TQA research using LLMs. We introduce various task setups and summarize available benchmarks based on task features. We categorize current modeling strategies according to the challenges they target, and analyze their strengths and limitations. Furthermore, we highlight underexplored but timely topics that have not been systematically covered in prior surveys. By unifying disparate research threads and identifying open problems, our survey offers a consolidated foundation for the TQA community, enabling a deeper understanding of the state of the art and guiding future developments in this rapidly evolving area."
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%0 Conference Proceedings
%T Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation
%A Zhou, Wei
%A Ma, Bolei
%A Friedrich, Annemarie
%A Mesgar, Mohsen
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhou-etal-2026-table
%X Table Question Answering (TQA) aims to answer natural language questions using tabular data, often accompanied by additional contexts, such as passages. The task spans diverse settings, varying in table representation, question/answer complexity, modality involved, and domain. While recent advances in large language models (LLMs) have driven substantial progress for TQA, the field still lacks a systematic organization and understanding of task formulations, core challenges, and methodological trends, particularly in light of emerging research directions, such as reinforcement learning. This survey addresses this gap by providing a comprehensive and structured overview of TQA research using LLMs. We introduce various task setups and summarize available benchmarks based on task features. We categorize current modeling strategies according to the challenges they target, and analyze their strengths and limitations. Furthermore, we highlight underexplored but timely topics that have not been systematically covered in prior surveys. By unifying disparate research threads and identifying open problems, our survey offers a consolidated foundation for the TQA community, enabling a deeper understanding of the state of the art and guiding future developments in this rapidly evolving area.
%U https://aclanthology.org/2026.acl-long.557/
%P 12161-12189
Markdown (Informal)
[Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation](https://aclanthology.org/2026.acl-long.557/) (Zhou et al., ACL 2026)
ACL