Fine-Tuning Models for Biomedical Relation Extraction

Abstract:Next-Generation Sequencing has revolutionized the study of genetic mutations, enabling large-scale investigations into their roles in disease development. However, extracting meaningful insights from the vast amount of biomedical literature remains a complex challenge that cannot be addressed manually. In this paper, we present pre-trained models (PTMs) for the automatic extraction of relations from biomedical text, specifically targeting the variant-phenotype domain. Our evaluation on the SNPPhenA corpus demonstrates that fine-tuning small BERT-based models, particularly DeBERTa, yields strong performance, approaching the current state-of-the-art (SOTA). Additionally, our results indicate that carefully fine-tuning Google's Gemini Pro 1.0 outperforms the existing SOTA for both sentence-level tasks (where the model processes only the target sentence) and abstract-level tasks (where the model processes the entire abstract).

Computation and Language (cs.CL)

arXiv:2609.20169 [cs.CL]

(or arXiv:2609.20169v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.20169 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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