Few-Shot Biomedical Relation Extraction with Large Language Models: A Viable Alternative to Supervised Learning?
Biomedical relation extraction (BioRE) is a key step in transforming biomedical literature into structured knowledge. 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 relations in a single model call. Experiments on the BioREDirect dataset reveal a clear precision-recall trade-off. Pairwise classification achieves higher rec...
| Authors | Jakob Mraz, Tomaž Curk, Blaž Zupan |
|---|---|
| Primary category | cs.CL |
| Published | 13 June 2026 |
| Last updated at source | 3 August 2026 |
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