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<p>Medical relation extraction aims to extract pairs of entities and their corresponding relations from unstructured text, which faces the challenge of a scarcity of labeled data. Zero-shot relation extraction can extract new relations not observed during training and alleviate the problem of scarce medical data. However, existing zero-shot relation extraction methods use semantic similarity matching with limited domain representation capability. In this paper, we introduces an <b>Uni</b>fied framework for zero-shot <b>M</b>edicial <b>R</b>elation <b>E</b>xtraction with Large Language Models (<b>UniMRE</b>), which leverages Large Language Models’ (LLMs) advanced contextual understanding capabilities to extract relation triplets in zero-shot setting. UniMRE employs a knowledge injection strategy to infuse medical knowledge into LLMs, which enables the generation of silver labels. These labels are used to retrieve relevant samples and relation rules, which are processed by a relation extraction agent. Based on their evaluation scores, high-confidence labels are incorporated into the sample library as gold labels, while low-confidence labels are refined and regenerated based on identified errors. Extensive experiments on medical datasets demonstrate that UniMRE outperforms baseline models, validating its effectiveness in extracting structured medical knowledge. </p>

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UniMRE: a unified framework for zero-shot medicial relation extraction with large language models

  • 期刊论文

  • 发表日期：

    2025年7月10日

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  • Yunlong Li,
  • Pengcheng Wu,
  • Aoze Zheng,
  • Yuting Li,
  • Hongying Zan,
  • Kunli Zhang

摘要

Medical relation extraction aims to extract pairs of entities and their corresponding relations from unstructured text, which faces the challenge of a scarcity of labeled data. Zero-shot relation extraction can extract new relations not observed during training and alleviate the problem of scarce medical data. However, existing zero-shot relation extraction methods use semantic similarity matching with limited domain representation capability. In this paper, we introduces an Unified framework for zero-shot Medicial Relation Extraction with Large Language Models (UniMRE), which leverages Large Language Models’ (LLMs) advanced contextual understanding capabilities to extract relation triplets in zero-shot setting. UniMRE employs a knowledge injection strategy to infuse medical knowledge into LLMs, which enables the generation of silver labels. These labels are used to retrieve relevant samples and relation rules, which are processed by a relation extraction agent. Based on their evaluation scores, high-confidence labels are incorporated into the sample library as gold labels, while low-confidence labels are refined and regenerated based on identified errors. Extensive experiments on medical datasets demonstrate that UniMRE outperforms baseline models, validating its effectiveness in extracting structured medical knowledge.