@inproceedings{lee-etal-2026-tabbridge,
    title = "{T}ab{B}ridge: Bridging Structure and Context for Accurate Table Reasoning",
    author = "Lee, Jeongwoo  and
      Lee, Eunsoo  and
      Kim, Jihie",
    editor = "Gupta, Vivek  and
      Ding, Kaize  and
      Kokel, Harsha  and
      Zhao, Yue  and
      Agarwal, Amit  and
      Wang, Yu  and
      Glass, Michael  and
      Zhang, Yu  and
      Srinivas, Kavitha  and
      Chen, Xiusi  and
      Hassanzadeh, Oktie  and
      Zhu, Qi  and
      Chang, Shuaichen  and
      Luo, Yuan",
    booktitle = "Proceedings of the First Workshop on Structured Understanding, Retrieval, and Generation in the {LLM} Era ({SURG}e{LLM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.surgellm-1.14/",
    pages = "219--240",
    ISBN = "979-8-89176-406-4",
    abstract = "Table reasoning remains challenging for Large Language Models (LLMs) as it requires integrating structured tabular information with natural language questions. Previous SQL-based approaches rely on surface-level alignment between question keywords and column headers, often generating queries with spurious or missing column mappings. We introduce TabBridge, a framework that incorporates both structural and contextual information for accurate table reasoning. TabBridge first generates a unified textual representation called Table Specification (TabSpec), preserving the structural information through row and column analysis. In order to ensure accuracy and consistency, we also employ a reconstruction-based evaluation mechanism to verify and refine the generated TabSpec. TabSpec is subsequently used to generate SQL aligned with the contextual intent of the question, enabling accurate interpretation of column semantics that are often overlooked by previous approaches.Across three public benchmarks, TabBridge shows consistent improvements over previous SQL-based methods, achieving 73.94{\%} accuracy on WikiTableQuestions (+5.3 pp over the previous state of the art). TabBridge also demonstrates robust performance across diverse LLM backbones, confirming its generalizability across model architectures. Our code is available at https://github.com/raylee0519/TabBridge."
}