MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation

TL;DR

MedBioLM is a specialized biomedical question-answering model that combines fine-tuning and retrieval-augmented generation to improve accuracy, factuality, and reasoning in medical and biological domains.

Contribution

The paper introduces MedBioLM, a novel domain-adapted biomedical LLM that integrates fine-tuning with retrieval-augmented generation for enhanced QA performance.

Findings

Fine-tuning improves accuracy on biomedical QA datasets.

Retrieval-augmented generation enhances factual consistency.

Model benefits medical research, education, and clinical decision support.

Abstract

Large Language Models (LLMs) have demonstrated impressive capabilities across natural language processing tasks. However, their application to specialized domains such as medicine and biology requires further optimization to ensure factual accuracy, reliability, and contextual depth. We introduce MedBioLM, a domain-adapted biomedical question-answering model designed to enhance both short-form and long-form queries. By integrating fine-tuning and retrieval-augmented generation (RAG), MedBioLM dynamically incorporates domain-specific knowledge, improving reasoning abilities and factual accuracy. To evaluate its effectiveness, we fine-tuned the model on diverse biomedical QA datasets, covering structured multiple-choice assessments and complex clinical reasoning tasks. Fine-tuning significantly improves accuracy on benchmark datasets, while RAG enhances factual consistency. These results highlight the potential of domain-optimized LLMs in advancing biomedical research, medical education, and clinical decision support.