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

• URL: http://arxiv.org/abs/2510.01612v1
• Date: Thu, 02 Oct 2025 02:49:09 GMT
• Title: RAG-BioQA Retrieval-Augmented Generation for Long-Form Biomedical Question Answering
• Authors: Lovely Yeswanth Panchumarthi, Sai Prasad Gudari, Atharva Negi, Praveen Raj Budime, Harsit Upadhya,
• Abstract summary: We present RAG-BioQA, a novel framework combining retrieval-augmented generation with domain-specific fine-tuning to produce evidence-based, long-form answers.<n> Experimental results on the PubMedQA dataset show significant improvements over baselines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• MedBioRAG: Semantic Search and Retrieval-Augmented Generation with Large Language Models for Medical and Biological QA [0.0]
  MedBioRAG is a retrieval-augmented model designed to improve biomedical question-answering (QA) performance.<n>We evaluate MedBioRAG across text retrieval, close-ended QA, and long-form QA tasks using benchmark datasets.
  arXiv  Detail & Related papers  (2025-12-10T15:43:25Z)
• BanglaMedQA and BanglaMMedBench: Evaluating Retrieval-Augmented Generation Strategies for Bangla Biomedical Question Answering [0.13048920509133807]
  This paper introduces the first large-scale datasets designed to evaluate reasoning and retrieval in medical artificial intelligence (AI)<n>The study applies and benchmarks several Retrieval-Augmented Generation (RAG) strategies, including Traditional, Zero-Shot Fallback, Agentic, Iterative Feedback, and Aggregate RAG.<n> Experimental results show that the Agentic RAG achieved the highest accuracy 89.54% with openai/gpt-oss-120b, outperforming other configurations and demonstrating superior rationale quality.
  arXiv  Detail & Related papers  (2025-11-06T17:15:33Z)
• Biomedical Literature Q&A System Using Retrieval-Augmented Generation (RAG) [0.0]
  This work presents a Biomedical Literature Question Answering (Q&A) system based on a Retrieval-Augmented Generation architecture.<n>The system integrates diverse sources, including PubMed articles, curated Q&A datasets, and medical encyclopedias.<n>The system supports both general medical queries and domain-specific tasks, with a focused evaluation on breast cancer literature.
  arXiv  Detail & Related papers  (2025-09-05T21:29:52Z)
• CaresAI at BioCreative IX Track 1 -- LLM for Biomedical QA [3.222047196930981]
  Large language models (LLMs) are increasingly evident for accurate question answering across various domains.<n>This paper presents our approach to the MedHopQA track of the BioCreative IX shared task.<n>Three experimental setups are explored: fine-tuning on combined short and long answers, short answers only, and long answers only.
  arXiv  Detail & Related papers  (2025-08-31T11:40:02Z)
• MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation [0.0]
  We introduce MedBioLM, a domain-adapted biomedical question-answering model.<n>By integrating fine-tuning and retrieval-augmented generation (RAG), MedBioLM dynamically incorporates domain-specific knowledge.<n>Fine-tuning significantly improves accuracy on benchmark datasets, while RAG enhances factual consistency.
  arXiv  Detail & Related papers  (2025-02-05T08:58:35Z)
• Causal Representation Learning from Multimodal Biomedical Observations [57.00712157758845]
  We develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical datasets.<n>Key theoretical contribution is the structural sparsity of causal connections between modalities.<n>Results on a real-world human phenotype dataset are consistent with established biomedical research.
  arXiv  Detail & Related papers  (2024-11-10T16:40:27Z)
• NeuroSym-BioCAT: Leveraging Neuro-Symbolic Methods for Biomedical Scholarly Document Categorization and Question Answering [0.14999444543328289]
  We introduce a novel approach that integrates an optimized topic modelling framework, OVB-LDA, with the BI-POP CMA-ES optimization technique for enhanced scholarly document abstract categorization. We employ the distilled MiniLM model, fine-tuned on domain-specific data, for high-precision answer extraction.
  arXiv  Detail & Related papers  (2024-10-29T14:45:12Z)
• BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers [48.21255861863282]
  BMRetriever is a series of dense retrievers for enhancing biomedical retrieval. BMRetriever exhibits strong parameter efficiency, with the 410M variant outperforming baselines up to 11.7 times larger.
  arXiv  Detail & Related papers  (2024-04-29T05:40:08Z)
• Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs [54.223394825528665]
  We develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models. We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low.
  arXiv  Detail & Related papers  (2023-12-21T14:26:57Z)
• BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks [68.39821375903591]
  Generalist AI holds the potential to address limitations due to its versatility in interpreting different data types. Here, we propose BiomedGPT, the first open-source and lightweight vision-language foundation model.
  arXiv  Detail & Related papers  (2023-05-26T17:14:43Z)
• BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs [46.87322157229728]
  We present PMC-15M, a novel dataset that is two orders of magnitude larger than existing biomedical multimodal datasets.<n> PMC-15M contains 15 million biomedical image-text pairs collected from 4.4 million scientific articles.<n>Based on PMC-15M, we have pretrained BiomedCLIP, a multimodal foundation model, with domain-specific adaptations tailored to biomedical vision-language processing.
  arXiv  Detail & Related papers  (2023-03-02T02:20:04Z)
• EBOCA: Evidences for BiOmedical Concepts Association Ontology [55.41644538483948]
  This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations. Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed to create a Knowledge Graph that can be used in real scenarios.
  arXiv  Detail & Related papers  (2022-08-01T18:47:03Z)
• BioIE: Biomedical Information Extraction with Multi-head Attention Enhanced Graph Convolutional Network [9.227487525657901]
  We propose Biomedical Information Extraction, a hybrid neural network to extract relations from biomedical text and unstructured medical reports. We evaluate our model on two major biomedical relationship extraction tasks, chemical-disease relation and chemical-protein interaction, and a cross-hospital pan-cancer pathology report corpus.
  arXiv  Detail & Related papers  (2021-10-26T13:19:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.