# BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine

arXiv: https://arxiv.org/abs/2405.00465 · HTML: https://mlsanity.com/paper/2405.00465/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 27 of its 46 references that are in the map.
- First posted on arXiv 1 May 2024.

## Placed in

- Retrieval-augmented generation / [Medical and biomedical RAG](https://mlsanity.com/rag/medical-and-biomedical-rag/)

## Builds on (most cited first, 27 of 27)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
- [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023)
- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [In-Context Retrieval-Augmented Language Models](https://mlsanity.com/paper/2302.00083/) (2023)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [RARR: Researching and Revising What Language Models Say, Using Language Models](https://mlsanity.com/paper/2210.08726/) (2022)
- [ToolQA: A Dataset for LLM Question Answering with External Tools](https://mlsanity.com/paper/2306.13304/) (2023)
- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [A Study of Generative Large Language Model for Medical Research and Healthcare](https://mlsanity.com/paper/2305.13523/) (2023)
- [Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey](https://mlsanity.com/paper/2305.18703/) (2023)
- [Drug Repurposing for COVID-19 via Knowledge Graph Completion](https://mlsanity.com/paper/2010.09600/) (2020)
- [OneRel:Joint Entity and Relation Extraction with One Module in One Step](https://mlsanity.com/paper/2203.05412/) (2022)
- [UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction](https://mlsanity.com/paper/2211.09039/) (2022)
- [How far is Language Model from 100% Few-shot Named Entity Recognition in Medical Domain](https://mlsanity.com/paper/2307.00186/) (2023)
- [Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking](https://mlsanity.com/paper/2307.01453/) (2023)
- [A Hierarchical N-Gram Framework for Zero-Shot Link Prediction](https://mlsanity.com/paper/2204.10293/) (2022)
- [Understand the Dynamic World: An End-to-End Knowledge Informed Framework for Open Domain Entity State Tracking](https://mlsanity.com/paper/2304.13854/) (2023)
- [Easy-to-Hard Learning for Information Extraction](https://mlsanity.com/paper/2305.09193/) (2023)
- [A Condensed Transition Graph Framework for Zero-shot Link Prediction with Large Language Models](https://mlsanity.com/paper/2402.10779/) (2024)

## Built on by (most cited first, 30 of 31)

- [Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2502.10708/) (2025)
- [A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions](https://mlsanity.com/paper/2406.03712/) (2024)
- [Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning](https://mlsanity.com/paper/2502.02871/) (2025)
- [RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models](https://mlsanity.com/paper/2412.02830/) (2024)
- [MatSciBench: Benchmarking the Reasoning Ability of Large Language Models in Materials Science](https://mlsanity.com/paper/2510.12171/) (2025)
- [CancerLLM: A Large Language Model in Cancer Domain](https://mlsanity.com/paper/2406.10459/) (2024)
- [Benchmarking Retrieval-Augmented Large Language Models in Biomedical NLP: Application, Robustness, and Self-Awareness](https://mlsanity.com/paper/2405.08151/) (2024)
- [RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models](https://mlsanity.com/paper/2501.05249/) (2025)
- [BioMedSearch: A Multi-Source Biomedical Retrieval Framework Based on LLMs](https://mlsanity.com/paper/2510.13926/) (2025)
- [GraPPI: A Retrieve-Divide-Solve GraphRAG Framework for Large-scale Protein-protein Interaction Exploration](https://mlsanity.com/paper/2501.16382/) (2025)
- [HealthContradict: Evaluating Biomedical Knowledge Conflicts in Language Models](https://mlsanity.com/paper/2512.02299/) (2025)
- [CliniBench: A Clinical Outcome Prediction Benchmark for Generative and Encoder-Based Language Models](https://mlsanity.com/paper/2509.26136/) (2025)
- [Knowledge-Augmented Long-CoT Generation for Complex Biomolecular Reasoning](https://mlsanity.com/paper/2511.08024/) (2025)
- [Can Frontier LLMs Replace Annotators in Biomedical Text Mining? Analyzing Challenges and Exploring Solutions](https://mlsanity.com/paper/2503.03261/) (2025)
- [RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosis](https://mlsanity.com/paper/2509.19980/) (2025)
- [An Alternative Trajectory for Generative AI](https://mlsanity.com/paper/2603.14147/) (2026)
- [Efficient and Effective Internal Memory Retrieval for LLM-Based Healthcare Prediction](https://mlsanity.com/paper/2604.07659/) (2026)
- [Injecting Structured Biomedical Knowledge into Language Models: Continual Pretraining vs. GraphRAG](https://mlsanity.com/paper/2604.16422/) (2026)
- [Detecci'on Autom'atica de Patolog'ias en Notas Cl'inicas en Espa~nol Combinando Modelos de Lenguaje y Ontolog'ias M'edicos](https://mlsanity.com/paper/2410.00616/) (2024)
- [Automatic detection of diseases in Spanish clinical notes combining medical language models and ontologies](https://mlsanity.com/paper/2412.03176/) (2024)
- [Conversational Text Extraction with Large Language Models Using Retrieval-Augmented Systems](https://mlsanity.com/paper/2501.09801/) (2025)
- [Biomedical Question Answering via Multi-Level Summarization on a Local Knowledge Graph](https://mlsanity.com/paper/2504.01309/) (2025)
- [Error-Aware Curriculum Learning for Biomedical Relation Classification](https://mlsanity.com/paper/2507.14374/) (2025)
- [BRAINCELL-AID: An Agentic AI Created Brain Cell Type Resource for Community Annotation](https://mlsanity.com/paper/2510.17064/) (2025)
- [CareMedEval dataset: Evaluating Critical Appraisal and Reasoning in the Biomedical Field](https://mlsanity.com/paper/2511.03441/) (2025)
- [Biomedical Hypothesis Explainability with Graph-Based Context Retrieval](https://mlsanity.com/paper/2511.05498/) (2025)
- [UniD3: A Knowledge Graph-Enhanced RAG Framework for Drug-Disease Discovery and Reasoning](https://mlsanity.com/paper/2606.01394/) (2026)
- [ANCHOR-RE: An Agentic Neuro-Symbolic Framework for Grounded Biomedical Relation Extraction](https://mlsanity.com/paper/2608.03154/) (2026)
- [Configurable Semantic Chunking for Biomedical Information Extraction in Retrieval-Augmented Generation](https://mlsanity.com/paper/2608.31139/) (2026)
- [DrugReason: Dynamic Multi-View Reasoning over Knowledge Graph and Language Evidence for Drug Repurposing](https://mlsanity.com/paper/2609.06779/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have swiftly emerged as vital resources for different applications in the biomedical and healthcare domains; however, these models encounter issues such as generating inaccurate information or hallucinations. Retrieval-augmented generation provided a solution for these models to update knowledge and enhance their performance. In contrast to previous retrieval-augmented LMs, which utilize specialized cross-attention mechanisms to help LLM encode retrieved text, BiomedRAG adopts a simpler approach by directly inputting the retrieved chunk-based documents into the LLM. This straightforward design is easily applicable to existing retrieval and language models, effectively bypassing noise information in retrieved documents, particularly in noise-intensive tasks. Moreover, we demonstrate the potential for utilizing the LLM to supervise the retrieval model in the biomedical domain, enabling it to retrieve the document that assists the LM in improving its predictions. Our experiments reveal that with the tuned scorer,textsc BiomedRAG attains superior performance across 5 biomedical NLP tasks, encompassing information extraction (triple extraction, relation extraction), text classification, link prediction, and question-answering, leveraging over 9 datasets. For instance, in the triple extraction task, textscBiomedRAG outperforms other triple extraction systems with micro-F1 scores of 81.42 and 88.83 on GIT and ChemProt corpora, respectively.