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Enhancing Relation Extraction from Biomedical Texts by Large Language Models

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Artificial Intelligence in HCI (HCII 2024)

Abstract

In this study, we propose a novel relation extraction method enhanced by large language models (LLMs). We incorporated three relation extraction models that leverage LLMs: (1) relation extraction via in-context few-shot learning with LLMs, (2) enhancing the sequence-to-sequence (seq2seq)-based full fine-tuned relation extraction by CoT reasoning explanations generated by LLMs, (3) enhancing the classification-based full fine-tuned relation extraction by entity descriptions that are automatically generated by LLMs. In the experiment, we shot that in-context few-shot learning with LLMs suffers in biomedical relation extraction tasks. We further show that entity explanations that are generated by LLMs can improve the performance of the classification-based relation extraction in the biomedical domain. Our proposed model achieved an F-score of 85.61% on the DDIExtraction-2013 dataset, which is competitive with the state-of-the-art models.

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Notes

  1. 1.

    https://ai.google.dev/.

  2. 2.

    https://hulat.inf.uc3m.es/DrugDDI/annotation_guidelines_ddi_corpus.pdf.

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Correspondence to Masaki Asada .

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Asada, M., Fukuda, K. (2024). Enhancing Relation Extraction from Biomedical Texts by Large Language Models. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2024. Lecture Notes in Computer Science(), vol 14736. Springer, Cham. https://doi.org/10.1007/978-3-031-60615-1_1

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  • DOI: https://doi.org/10.1007/978-3-031-60615-1_1

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