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Summary

This summary is machine-generated.

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.