LLMs show promise for low-resource biomedical relation extraction

Researchers explored the use of large language models (LLMs) for few-shot biomedical relation extraction, a task crucial for structuring knowledge from biomedical literature. Their experiments on the BioREDirect dataset compared two LLM task formulations: pairwise classification and joint generation. While pairwise classification offered higher recall, joint generation proved more precise and efficient. The best LLM approach achieved a micro-F1 score of 0.44, surpassing prior few-shot results and demonstrating strong performance on rare relation types, though it fell short of fully supervised methods. AI

RANK_REASON Academic paper published on arXiv detailing a new research methodology. [lever_c_demoted from research: ic=1 ai=1.0]

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• Hugging Face
• Large Language Models

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