@inproceedings{ram-etal-2026-transfer,
    title = "Transfer Learning for Generalizable Automated {LLM} Improvement Pipeline for {IVR} Navigation",
    author = "Ram, Vishal Sankar  and
      Kushner, Jason  and
      Paldhe, Manas  and
      Son, Youngseo",
    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.136/",
    pages = "2012--2030",
    ISBN = "979-8-89176-394-4",
    abstract = "Administrative tasks in the healthcare domain share linguistic commonalities, but it can be time-consuming to manually design LLM prompts for each use case. When calling health insurers, interactive voice response (IVR) systems cause delays in patient care and increase provider burnout due to complex routing and long hold times. Thus, IVR navigation models can offer significant time savings and reduce barriers to care. We propose a production-quality automated LLM pipeline which leverages a small number of human-labeled ground truth datasets to transfer specialized prompts from one task to another; specifically, we perform a cross-task transfer of our IVR navigation logic, adapting the prompt from reaching the claims department to reaching the patient benefit department. Our approach reduces prompt complexity by up to 80{\%} and obtains 82{\%} turn-level accuracy in real-world industrial healthcare settings, surpassing a human-designed prompt at 79{\%}."
}