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

Name: Few-Shot Biomedical Relation Extraction with Large Language Models: A Viable Alternative to Supervised Learning?
Uploaded: 2026-10-23T09:37:56.292784+00:00
Duration: 811 s
Description: The study investigated using prompt-based learning with large language models (LLMs) for few-shot biomedical relation extraction (BioRE), comparing pairwise classification and joint generation tasks. Joint generation proved more precise and computationally efficient, achieving a micro-F1 score of 0.44. This shows LLMs are a viable, low-resource alternative to costly supervised learning methods.

The study investigated using prompt-based learning with large language models (LLMs) for few-shot biomedical relation extraction (BioRE), comparing pairwise classification and joint generation tasks. Joint generation proved more precise and computationally efficient, achieving a micro-F1 score of 0.44. This shows LLMs are a viable, low-resource alternative to costly supervised learning methods.

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