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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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REaMA: Building Biomedical Relation Extraction Specialized Large Language Models Through Instruction Tuning.

Yidan Zhang, Junlin Yu, Guobo Li

    IEEE Transactions on Neural Networks and Learning Systems
    |August 20, 2025
    PubMed
    Summary

    New REaMA models, specialized for biomedical relation extraction (BioRE), significantly outperform existing methods. Instruction-tuning general large language models (LLMs) with the REInstruct dataset enhances their biomedical IE capabilities.

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    Area of Science:

    • Biomedical informatics
    • Natural Language Processing
    • Artificial Intelligence

    Background:

    • Biomedical relation extraction (BioRE) is crucial for understanding biomedical literature.
    • General large language models (LLMs) show limitations in specialized BioRE tasks.
    • Existing open-source LLMs underperform compared to state-of-the-art (SOTA) methods in BioRE.

    Purpose of the Study:

    • To develop specialized large language models (LLMs) for biomedical relation extraction (BioRE).
    • To address the performance gap of general LLMs in BioRE tasks.
    • To introduce a multitask instruction-tuning framework and dataset (REInstruct) for BioRE.

    Main Methods:

    • Developed a multitask instruction-tuning framework using the REInstruct dataset (150,000 pairs).
    • Created REaMA, a series of open-source LLMs (7B and 13B parameters) for BioRE.
    • Evaluated REaMA models on seven diverse BioRE datasets.

    Main Results:

    • REaMA models demonstrate strong performance across multiple BioRE datasets.
    • REaMA-2-13B surpasses the SOTA method on five out of seven datasets.
    • Instruction-tuning with REInstruct effectively enhances LLMs' BioRE capabilities.

    Conclusions:

    • The REInstruct dataset and multitask instruction-tuning framework significantly improve LLM performance in BioRE.
    • REaMA models represent a substantial advancement in open-source BioRE tools.
    • Incorporating Chain of Thought (CoT) further boosts REaMA's generalization ability.