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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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

Ran Xu¹, Wenqi Shi², Yue Yu²

¹Emory University.

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing

|March 2, 2026

Summary

This summary is machine-generated.

BMRetriever enhances biomedical retrieval using unsupervised pre-training and instruction fine-tuning. This model shows strong performance and parameter efficiency, aiding knowledge-intensive biomedical tasks.

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Area of Science:

Biomedical Informatics
Information Retrieval
Natural Language Processing

Background:

Effective biomedical retrieval models are crucial for knowledge-intensive tasks.
Challenges include limited annotated data and computational resources.

Purpose of the Study:

To develop BMRetriever, a series of dense retrievers for improved biomedical information retrieval.
To address data scarcity and computational limitations in the field.

Main Methods:

Unsupervised pre-training on large biomedical corpora.
Instruction fine-tuning using labeled datasets and synthetic data pairs.
Development of parameter-efficient dense retriever variants (410M and 2B parameters).

Main Results:

BMRetriever demonstrated efficacy across 5 biomedical tasks and 11 datasets.
The 410M variant outperformed significantly larger baselines (up to 11.7x).
The 2B variant achieved performance comparable to models over 5B parameters.

Conclusions:

BMRetriever offers a powerful and efficient solution for biomedical retrieval.
The released model checkpoints and data promote transparency and reproducibility.
BMRetriever can be applied to new biomedical domains and tasks.