@inproceedings{heidenreich-etal-2025-page,
    title = "Page Stream Segmentation with {LLM}s: Challenges and Applications in Insurance Document Automation",
    author = "Heidenreich, Hunter  and
      Dalvi, Ratish  and
      Verma, Nikhil  and
      Getachew, Yosheb",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven  and
      Darwish, Kareem  and
      Agarwal, Apoorv",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2025.coling-industry.26/",
    pages = "305--317",
    abstract = "Page Stream Segmentation (PSS) is critical for automating document processing in industries like insurance, where unstructured document collections are common. This paper explores the use of large language models (LLMs) for PSS, applying parameter-efficient fine-tuning to real-world insurance data. Our experiments show that LLMs outperform baseline models in page- and stream-level segmentation accuracy. However, stream-level calibration remains challenging, especially for high-stakes applications. We evaluate post-hoc calibration and Monte Carlo dropout, finding limited improvement. Future work will integrate active learning to enhance model calibration and support deployment in practical settings."
}