razreshili at ArchEHR-QA 2025: Contrastive Fine-Tuning for Retrieval-Augmented Biomedical QA
Year: 2025
Type: conference-paper
Abstract: We present a retrieval-augmented system for the ArchEHR-QA 2025 shared task, which focuses on generating concise, medically accurate answers to clinical questions based on a patient's electronic health record (EHR).A key challenge is following a strict citation format that references relevant sentence IDs.To improve retrieval, we fine-tuned an all-MiniLM-L6-v2 embedding model using contrastive learning on over 2,300 question-sentence triplets, with DoRA for efficient adaptation.Sentences were selected using cosine similarity thresholds and passed into a quantized Mistral-7B-Instruct model along with a structured prompt.Our system achieved similar relevance to the baseline but lower overall performance (19.3 vs. 30.7),due to issues with citation formatting and generation quality.We discuss limitations such as threshold tuning, prompt-following ability, and model size, and suggest future directions for improving structured biomedical QA.
Author: Arina Zemchyk
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412889117