UniBuc-SB at ArchEHR-QA 2025: A Resource-Constrained Pipeline for Relevance Classification and Grounded Answer Synthesis
Sebastian Balmus, Dura Bogdan, Ana Sabina Uban · 2025
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 a 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.