@inproceedings{zhang-etal-2026-smart,
    title = "{SMART}: Semantic Header Flattening and Pseudo-Code-Style Reasoning for {LLM}-based Complex Table Question Answering",
    author = "Zhang, Yongshuo  and
      Chen, Jixiong  and
      xu, Kaihe  and
      Wei, Wei  and
      Zou, Shihao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1474/",
    pages = "29483--29499",
    ISBN = "979-8-89176-395-1",
    abstract = "Complex table question answering (TQA) remains challenging, as real-world table, usually designed for human readability with multi-level headers and fragmented hierarchical semantics, largely hindering large language models (LLMs) from accurately aligning conditions, attributes, and values during reasoning. Existing approaches typically rely on handcrafted table linearization or prompts, forcing LLMs to infer header hierarchies, which frequently leads to brittle reasoning and hallucinations. To this end, we propose SMART, a unified framework that explicitly decouples table structure understanding from reasoning execution. SMART consists of three components: Semantic Header Flattening for converting multi-level headers into explicit single-level descriptors, Global Understanding for capturing holistic table{--}question semantics, and Pseudo-Code-Style Reasoning for structured, step-by-step inference with external validation. Extensive experiments on multiple benchmarks demonstrate that SMART substantially improves both the accuracy and robustness of complex TQA, achieving state-of-the-art performance."
}