Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Mar 31, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.3K

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
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Intranasal ectopic tooth: An unusual diagnostic finding with review of literature.

Radiology case reports·2026
Same author

Secondary hemophagocytic lymphohistiocytosis following bacterial pneumonia managed with dexamethasone monotherapy: a case report.

Annals of medicine and surgery (2012)·2026
Same author

Automated Behavior Analysis in the Novel Object Recognition Test.

Neurocomputing·2026
Same author

Toward a Comprehensive Pea Aphid Saliva-Proteomewith Insights from Transcripts from the Whitefly <i>Bemisia tabaci</i>.

Biochemistry & molecular biology journal·2026
Same author

Rodent Social Behavior Recognition Using a Global Context-Aware Vision Transformer Network.

AI (Basel, Switzerland)·2026
Same author

Deep learning for sorghum yield forecasting using uncrewed aerial systems and lab-derived imagery.

Plant phenomics (Washington, D.C.)·2026

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

Related Experiment Videos

Last Updated: Mar 31, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.3K
  • 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.