@inproceedings{balmus-etal-2025-unibuc,
    title = "{U}ni{B}uc-{SB} at {A}rch{EHR}-{QA} 2025: A Resource-Constrained Pipeline for Relevance Classification and Grounded Answer Synthesis",
    author = "Balmus, Sebastian  and
      Bogdan, Dura  and
      Uban, Ana Sabina",
    editor = "Soni, Sarvesh  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.bionlp-share.7/",
    doi = "10.18653/v1/2025.bionlp-share.7",
    pages = "62--68",
    ISBN = "979-8-89176-276-3",
    abstract = "We describe the UniBuc-SB submission to the ArchEHR-QA shared task, which involved generating grounded answers to patient questions based on electronic health records. Our system exceeded the performance of the provided baseline, achieving higher performance in generating contextually relevant responses. Notably, we developed our approach under constrained computational resources, utilizing only a single NVIDIA RTX 4090 GPU. We refrained from incorporating any external datasets, relying solely on the limited training data supplied by the organizers. To address the challenges posed by the low-resource setting, we leveraged off-the-shelf pre-trained language models and fine-tuned them minimally, aiming to maximize performance while minimizing overfitting."
}