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
The rapid advancement and growing sophistication of large language models are enabling their application to more specialized and data-intensive tasks like biomedical relation extraction, where traditional supervised methods face scalability challenges.
This development suggests that LLMs could significantly reduce the cost and time associated with generating structured knowledge from scientific literature, accelerating research and development in critical fields.
The reliance on large, manually annotated datasets for complex information extraction, particularly in biomedical domains, may diminish as few-shot LLM approaches prove increasingly viable.
- · AI/ML researchers
- · Pharmaceutical companies
- · Biotech startups
- · Healthcare providers
- · Data annotation services
- · Traditional supervised ML model developers
Increased efficiency in transforming unstructured biomedical text into actionable insights.
Faster drug discovery processes and more personalized medical treatments due to improved knowledge extraction.
Potential for new AI-powered diagnostic tools and research platforms that leverage readily available, structured biomedical knowledge at scale.
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Read at arXiv cs.CL