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Semi-Supervised Relation Extraction Informed by Area Under the Margin Ranking and Large Language Models.

Nikita Gautam1, Bipin Paudel1, Doina Caragea1

  • 1Department of Computer Science Kansas State University Manhattan, Kansas, USA.

Proceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics
|March 30, 2026
PubMed
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This study introduces RE-AUM-LLM, a novel approach for low-resource relation extraction. It improves pseudo-label quality using Area Under the Margin (AUM) and Large Language Models (LLMs), achieving state-of-the-art results.

Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Relation extraction is crucial for knowledge discovery and graph construction.
  • Acquiring labeled data for relation extraction is challenging, especially in low-resource scenarios.
  • Semi-supervised methods like self-training offer solutions but can suffer from noisy pseudo-labels.

Purpose of the Study:

  • To address the limitations of traditional self-training in low-resource relation extraction.
  • To introduce a novel model, RE-AUM-LLM, for generating high-quality pseudo-labels.
  • To improve the performance of relation extraction models with limited labeled data.

Main Methods:

  • Implemented a self-training framework combined with Area Under the Margin (AUM).
Keywords:
LLMarea under the marginlow resource settingrelation extractionself-trainingsemi-supervised learning

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  • Integrated Large Language Models (LLMs), specifically Llama 3.1, for pseudo-label generation.
  • Evaluated the RE-AUM-LLM model on two benchmark datasets for low-resource relation extraction.

    • Main Results:

    • The RE-AUM-LLM model achieved state-of-the-art performance on benchmark datasets.
    • The proposed approach significantly improved relation extraction accuracy in low-resource settings.
    • High-quality pseudo-labels generated by RE-AUM-LLM mitigated noise issues in self-training.

    Conclusions:

    • RE-AUM-LLM offers a powerful solution for low-resource relation extraction.
    • Combining AUM and LLMs enhances pseudo-label quality and model performance.
    • The approach demonstrates significant advancements in leveraging limited data for relation extraction tasks.