@inproceedings{khoja-etal-2025-weaver,
    title = "Weaver: Interweaving {SQL} and {LLM} for Table Reasoning",
    author = "Khoja, Rohit  and
      Gupta, Devanshu  and
      Fu, Yanjie  and
      Roth, Dan  and
      Gupta, Vivek",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1436/",
    pages = "28270--28296",
    ISBN = "979-8-89176-332-6",
    abstract = "Querying tables with unstructured data is challenging due to the presence of text (or image), either embedded in the table or in external paragraphs, which traditional SQL struggles to process, especially for tasks requiring semantic reasoning. While Large Language Models (LLMs) excel at understanding context, they face limitations with long input sequences. Existing approaches that combine SQL and LLM typically rely on rigid, predefined workflows, limiting their adaptability to complex queries. To address these issues, we introduce Weaver, a modular pipeline that dynamically integrates SQL and LLM for table-based question answering (Table QA). Weaver generates a flexible, step-by-step plan that combines SQL for structured data retrieval with LLMs for semantic processing. By decomposing complex queries into manageable subtasks, Weaver improves accuracy and generalization. Our experiments show that consistently outperforms state-of-the-art methods across four Table QA datasets, reducing both API calls and error rates."
}