Few-Shot Biomedical Relation Extraction with Large Language Models: A Viable Alternative to Supervised Learning?

arXiv:2606.15412v1 Announce Type: new Abstract: Biomedical relation extraction (BioRE) is a key step in transforming biomedical literature into structured knowledge. However, most existing approaches rely on supervised models trained on costly annotated datasets, limiting their scalability and adaptability across relation types and domains. We investigate few-shot BioRE using prompt-based learning with large language models (LLMs) and compare two task formulations: pairwise classification, which predicts relations for individual entity pairs, and joint generation, which extracts multiple relat

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