Work

UniBuc-SB at ArchEHR-QA 2025: A Resource-Constrained Pipeline for Relevance Classification and Grounded Answer Synthesis

Year: 2025

Type: conference-paper

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 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.

Source: Workshop on Biomedical Language Processing (BioNLP)

DOI: 10.18653/v1/2025.bionlp-share.7

Authors: Sebastian Balmus, Dura Bogdan, Ana Sabina Uban

Institutions: University of Bucharest, National Institute for Research and Development in Informatics - ICI Bucharest

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Ministerul Cercetării, Inovării şi Digitalizării, Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii, Colegiul Consultativ pentru Cercetare-Dezvoltare şi Inovare

OpenAlex ID: W4412889152