# BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers

arXiv: https://arxiv.org/abs/2404.18443 · HTML: https://mlsanity.com/paper/2404.18443/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 60 of its 98 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 30 of 60)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [ELI5: Long Form Question Answering](https://mlsanity.com/paper/1907.09190/) (2019)
- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [Large Dual Encoders Are Generalizable Retrievers](https://mlsanity.com/paper/2112.07899/) (2021)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2010.08191/) (2020)
- [LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders](https://mlsanity.com/paper/2404.05961/) (2024)
- [Text and Code Embeddings by Contrastive Pre-Training](https://mlsanity.com/paper/2201.10005/) (2022)
- [NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark](https://mlsanity.com/paper/2310.18018/) (2023)
- [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://mlsanity.com/paper/2212.09741/) (2022)

## Built on by (most cited first, 30 of 38)

- [A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery](https://mlsanity.com/paper/2406.10833/) (2024)
- [HYDRA: Model Factorization Framework for Black-Box LLM Personalization](https://mlsanity.com/paper/2406.02888/) (2024)
- [Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark](https://mlsanity.com/paper/2407.13766/) (2024)
- [SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains](https://mlsanity.com/paper/2410.17952/) (2024)
- [Efficient Knowledge Injection in LLMs via Self-Distillation](https://mlsanity.com/paper/2412.14964/) (2024)
- [jina-reranker-v3: Last but Not Late Interaction for Listwise Document Reranking](https://mlsanity.com/paper/2509.25085/) (2025)
- [OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models](https://mlsanity.com/paper/2412.15235/) (2024)
- [Kanana: Compute-efficient Bilingual Language Models](https://mlsanity.com/paper/2502.18934/) (2025)
- [Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration](https://mlsanity.com/paper/2504.04915/) (2025)
- [Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models](https://mlsanity.com/paper/2408.13808/) (2024)
- [Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications](https://mlsanity.com/paper/2501.02460/) (2025)
- [RAMIE: Retrieval-Augmented Multi-task Information Extraction with Large Language Models on Dietary Supplements](https://mlsanity.com/paper/2411.15700/) (2024)
- [End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning](https://mlsanity.com/paper/2508.15746/) (2025)
- [AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels](https://mlsanity.com/paper/2410.20050/) (2024)
- [RAGRouter: Learning to Route Queries to Multiple Retrieval-Augmented Language Models](https://mlsanity.com/paper/2505.23052/) (2025)
- [Decoding Dense Embeddings: Sparse Autoencoders for Interpreting and Discretizing Dense Retrieval](https://mlsanity.com/paper/2506.00041/) (2025)
- [A Collection of Innovations in Medical AI for patient records in 2024](https://mlsanity.com/paper/2503.05768/) (2025)
- [FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes](https://mlsanity.com/paper/2506.03278/) (2025)
- [Intermediate Distillation: Data-Efficient Distillation from Black-Box LLMs for Information Retrieval](https://mlsanity.com/paper/2406.12169/) (2024)
- [DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management](https://mlsanity.com/paper/2505.15856/) (2025)
- [RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation](https://mlsanity.com/paper/2507.20059/) (2025)
- [Tracing the Data Trail: A Survey of Data Provenance, Transparency and Traceability in LLMs](https://mlsanity.com/paper/2601.14311/) (2026)
- [DMRetriever: A Family of Models for Improved Text Retrieval in Disaster Management](https://mlsanity.com/paper/2510.15087/) (2025)
- [Data-Efficient Biomedical In-Context Learning: A Diversity-Enhanced Submodular Perspective](https://mlsanity.com/paper/2508.08140/) (2025)
- [Exploring the Capabilities of Large Language Model Encoders for Image-Text Retrieval in Chest X-rays](https://mlsanity.com/paper/2509.15234/) (2025)
- [GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models](https://mlsanity.com/paper/2509.16502/) (2025)
- [REPAIR: Resolving Long-Tail Confusion in Scientific Retrievers via Fact-Verified Iterative Refinement](https://mlsanity.com/paper/2609.18262/) (2026)
- [Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework](https://mlsanity.com/paper/2501.09631/) (2025)
- [DR.EHR: Dense Retrieval for Electronic Health Record with Knowledge Injection and Synthetic Data](https://mlsanity.com/paper/2507.18583/) (2025)
- [Ontology-Guided Query Expansion for Biomedical Document Retrieval using Large Language Models](https://mlsanity.com/paper/2508.11784/) (2025)

## Abstract (arXiv)

Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedical tasks but still challenging due to the deficiency of sufficient publicly annotated biomedical data and computational resources. We present BMRetriever, a series of dense retrievers for enhancing biomedical retrieval via unsupervised pre-training on large biomedical corpora, followed by instruction fine-tuning on a combination of labeled datasets and synthetic pairs. Experiments on 5 biomedical tasks across 11 datasets verify BMRetriever's efficacy on various biomedical applications. BMRetriever also exhibits strong parameter efficiency, with the 410M variant outperforming baselines up to 11.7 times larger, and the 2B variant matching the performance of models with over 5B parameters. The training data and model checkpoints are released at urlhttps://huggingface.co/BMRetriever to ensure transparency, reproducibility, and application to new domains.