@inproceedings{shrestha-etal-2026-hemolix,
    title = "{H}emolix.{T}ab{G}en: Optimized Table Generation from Documents",
    author = "Shrestha, Gyanendra  and
      Ivanov, Todor  and
      Vemireddy, Karthik  and
      Pyayt, Anna  and
      Gubanov, Michael",
    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 (Volume 6: Industry Track)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-form-platform/2026.acl-industry.73/",
    pages = "1055--1066",
    ISBN = "979-8-89176-394-4",
    abstract = "Modern Data Lakes contain vast and heterogeneous document collections, making table generation from documents a persistent and nontrivial challenge. Traditional approaches are often rigid {---} i.e. domain-specific, require extensive supervision, or are limited to set of pre-defined schemas; LLM-based approaches are more flexible, but typically suffer from hallucinations, non-determinism, and high computational costs. To overcome these limitations, we introduce Hemolix.TabGen, a novel scalable LLM-based table generation systemthat comprehends documents and generates Bi-dimensional tables based on the entire document content. We evaluated TabGen on 4 publicly available datasets spanning multiple domains and observed an Average Precision delta up to 30{\%} compared to vanilla LLMs"
}