@inproceedings{wang-etal-2025-plugging,
    title = "Plugging Schema Graph into Multi-Table {QA}: A Human-Guided Framework for Reducing {LLM} Reliance",
    author = "Wang, Xixi  and
      Costa, Miguel  and
      Kovaceva, Jordanka  and
      Wang, Shuai  and
      Pereira, Francisco C.",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.311/",
    doi = "10.18653/v1/2025.findings-emnlp.311",
    pages = "5829--5842",
    ISBN = "979-8-89176-335-7",
    abstract = "Large language models (LLMs) have shown promise in table Question Answering (Table QA). However, extending these capabilities to multi-table QA remains challenging due to unreliable schema linking across complex tables. Existing methods based on semantic similarity work well only on simplified hand-crafted datasets and struggle to handle complex, real-world scenarios with numerous and diverse columns. To address this, we propose a graph-based framework that leverages human-curated relational knowledge to explicitly encode schema links and join paths. Given a natural language query, our method searches on graph to construct interpretable reasoning chains, aided by pruning and sub-path merging strategies to enhance efficiency and coherence. Experiments on both standard benchmarks and a realistic, large-scale dataset demonstrate the effectiveness of our approach. To our knowledge, this is the first multi-table QA system applied to truly complex industrial tabular data."
}