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arXiv · 2510.01612

RAG-BioQA: A Retrieval-Augmented Generation Framework for Long-Form Biomedical Question Answering

Lovely Yeswanth Panchumarthi·Sumalatha Saleti·Sai Prasad Gudari·Atharva Negi·Praveen Raj Budime·Harsit Upadhya
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Abstract

The rapidly growth of biomedical literature creates challenges acquiring specific medical information. Current biomedical question-answering systems primarily focus on short-form answers, failing to provide comprehensive explanations necessary for clinical decision-making. We present RAG-BioQA, a retrieval-augmented generation framework for long-form biomedical question answering. Our system integrates BioBERT embeddings with FAISS indexing for retrieval and a LoRA fine-tuned FLAN-T5 model for answer generation. We train on 181k QA pairs from PubMedQA, MedDialog, and MedQuAD, and evaluate on a held-out PubMedQA test set. We compare four retrieval strategies: dense retrieval (FAISS), BM25, ColBERT, and MonoT5. Our results show that domain-adapted dense retrieval outperforms zero-shot neural re-rankers, with the best configuration achieving 0.24 BLEU-1 and 0.29 ROUGE-1. Fine-tuning improves BERTScore by 81\% over the base model. We release our framework to support reproducible biomedical QA research.

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Lovely Yeswanth Panchumarthi, Sumalatha Saleti, Sai Prasad Gudari, Atharva Negi, Praveen Raj Budime, Harsit Upadhya. 2025-10-02. RAG-BioQA: A Retrieval-Augmented Generation Framework for Long-Form Biomedical Question Answering. https://arxiv.org/abs/2510.01612

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