Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (3): 295-306.doi: 10.11896/jsjkx.250900006

• Artificial Intelligence • Previous Articles     Next Articles

Survey of Table Question Answering Research

WU Xianjie1, LI Tongliang2, LI Zhoujun1   

  1. 1 School of Computer Science and Engineering, Beihang University, Beijing 100191, China
    2 College of Computer Science, Beijing Information Science and Technology University, Beijing 102206, China
  • Received:2025-09-01 Revised:2025-11-21 Published:2026-03-12
  • About author:WU Xianjie,born in 1993,Ph.D candidate.His main research interests include knowledge understanding and reasoning based on large language models.
    LI Zhoujun,born in 1963,Ph.D,professor,is a senior member of CCF(No.06618S).His main research interests include artificial intelligence,natural language processing,and network and information security.
  • Supported by:
    National Natural Science Foundation of China(62276017,62406033,U1636211,61672081) and State Key Laboratory of Complex & Critical Software Environment(SKLCCSE-2024ZX-18).

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Abstract: Tables serve as essential data carriers,capable of efficiently storing large volumes of high-value information.They are widely used across domains such as economics,finance,scientific research,and more.Table question answering(TableQA) aims to automatically derive answers from tabular data in response to natural language queries,representing a key research direction at the intersection of natural language processing and data analysis.Compared to traditional text-based or knowledge-base question answering,TableQA presents greater challenges,as it requires not only natural language understanding but also the interpretation of two-dimensional table structures,numerical computations,and complex logical reasoning.In recent years,the continuous deve-lopment of diverse datasets has driven steady progress in TableQA research.The field has evolved from early rule-based and template-based approaches to statistical learning and neural network models,and more recently,to the integration of pre-trained language models,resulting in consistent performance improvements.Notably,the emergence of large language models(LLMs) has ushered in a new phase of development.Leveraging their strong cross-task generalization and reasoning capabilities,LLMs have accelerated innovation and fostered new research paradigms in TableQA.This paper systematically reviews the evolution and representative methods of TableQA,with a particular emphasis on recent advances enabled by LLMs.It also outlines the key challenges currently facing the field and provides a forward-looking perspective on future research directions.

Key words: Table question answering, Table reasoning, Large language model, Natural language processing

CLC Number: 

  • TP391
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