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Related Experiment Video

Updated: May 5, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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SKORE: Skill-oriented extraction engine from job advertisements using large language models.

Pedro C Menezes1, Kenzo M Sakiyama2, Ricardo M Marcacini2

  • 1UFOPA, Federal University of Western Pará, Brazil.

Methodsx
|May 4, 2026
PubMed
Summary

We developed the Skill-Oriented Extraction Engine (SKORE) to extract and categorize skills from online job ads. SKORE uses Large Language Models (LLMs) and human input to improve skill analysis for career transitions and job ad creation.

Keywords:
Job ads analysisJob postsLLMLabor marketMarket IntelligenceMarket trend analysis

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Area of Science:

  • Labor market analysis
  • Natural Language Processing (NLP)
  • Data extraction

Background:

  • The digital labor market generates vast Online Job Advertisements (OJAs) containing valuable skill data.
  • Extracting skills from unstructured OJAs is challenging due to limitations in existing lexicon-based, supervised, and unsupervised methods.
  • Large Language Models (LLMs) offer advanced text analysis capabilities but require structured application.

Purpose of the Study:

  • To introduce the Skill-Oriented Extraction Engine (SKORE), a novel framework for skills extraction, normalization, and categorization from OJAs.
  • To address the limitations of current skill extraction methods by integrating LLMs with human-in-the-loop approaches.
  • To provide a model-agnostic framework adaptable to diverse domains and data sources.

Main Methods:

  • Development of the Skill-Oriented Extraction Engine (SKORE) framework.
  • Integration of Large Language Models (LLMs) for semantic text analysis.
  • Incorporation of human-in-the-loop mechanisms for refinement and validation.
  • Application of a proof-of-concept case study on job-post data.

Main Results:

  • SKORE effectively extracts, normalizes, and categorizes both technical and behavioral skills from OJAs.
  • The framework demonstrates adaptability across different domains and data sources.
  • The proof-of-concept validated the framework's capability in capturing relevant market skills.

Conclusions:

  • SKORE offers a robust solution for analyzing skills in the digital labor market.
  • The framework empowers professionals with career transition insights and aids in creating market-aligned job advertisements.
  • SKORE is applicable for educational institutions to adapt curricula to evolving market demands.