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REaMA: Building Biomedical Relation Extraction Specialized Large Language Models Through Instruction Tuning

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REaMA models demonstrate superior performance in biomedical relation extraction compared to general large language models.
The introduction of the REInstruct dataset facilitates effective instruction tuning, yielding higher quality outputs.
Larger REaMA-2-13B exceeds state-of-the-art performance on five out of seven benchmark datasets.
Integrating a chain of thought approach further boosts the generalization abilities of the models.

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Multitask instruction tuning enhances performance in biomedical relation extraction tasks, indicating improved capabilities in large language models.

Zhang et al. (2025) studied this question.

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Multitask instruction tuning enhances performance in biomedical relation extraction tasks, indicating improved capabilities in large language models.