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October 5, 2025Open Access

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

Key Points

RAG-BioQA significantly improves long-form biomedical question answering with advanced synthesis methods.
The model integrated BioBERT embeddings and FAISS indexing, yielding better performance in access and accuracy.
Utilizing strategies like BM25 and MonoT5 led to optimized context selection before answer generation.
Our experimental analysis on the PubMedQA dataset showed notable gains across multiple evaluation metrics.

Abstract

The exponential growth of biomedical literature creates significant challenges for accessing precise medical information. Current biomedical question-answering systems primarily focus on short-form answers, failing to provide the comprehensive explanations necessary for clinical decision-making. We present RAG-BioQA, a novel framework combining retrieval-augmented generation with domain-specific fine-tuning to produce evidence-based, long-form biomedical answers. Our approach integrates BioBERT embeddings with FAISS indexing and compares various re-ranking strategies (BM25, ColBERT, MonoT5) to optimize context selection before synthesizing evidence through a fine-tuned T5 model. Experimental results on the PubMedQA dataset show significant improvements over baselines, with our best model achieving substantial gains across BLEU, ROUGE, and METEOR metrics, advancing the state of accessible, evidence-based biomedical knowledge retrieval.

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