@inproceedings{ho-etal-2026-schemarag,
    title = "{S}chema{RAG}: Dynamic Large Schema Reduction for {LLM}-driven Structured Information Extraction",
    author = "Ho, Sin Yu Bonnie  and
      Coles, Arlie  and
      Larsson, Erik  and
      Marshall, Eric  and
      Bodenstab, Nathan  and
      Vozila, Paul",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.78/",
    pages = "1114--1127",
    ISBN = "979-8-89176-394-4",
    abstract = "Extracting structured data from unstructured text using large language models (LLMs) becomes challenging when the target schemas are large and complex. In such cases, including the full schema in the prompt increases cost and latency, risks lost-in-the-middle performance degradation, and can exceed context length limits. We propose SchemaRAG, a retrieval-augmented generation (RAG) framework that dynamically prunes the output schema space for schema-conditioned information extraction tasks by leveraging schema metadata and few-shot examples (when available). We evaluate SchemaRAG on real-world healthcare and e-commerce datasets. Our results show that SchemaRAG can achieve up to an 8.8{\%} increase in micro-F1, a 47{\%} reduction in latency, and a 48{\%} reduction in token costs, demonstrating its practicality for large-schema extraction."
}