Using Large Language Models and Recruiter Expertise for Optimized Multilingual Job Offer – Applicant CV Matching

Using Large Language Models and Recruiter Expertise for Optimized Multilingual Job Offer – Applicant CV Matching

Hamit Kavas, Marc Serra-Vidal, Leo Wanner

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Demo Track. Pages 8696-8699. https://doi.org/10.24963/ijcai.2024/1011

In the context of the increasingly globalised economy and labour market, recruitment agencies face the challenge to deal with a magnitude of job offers and job applications written in a variety of languages, formats, and styles. Quite often, this leads to a suboptimal evaluation of the CVs of job seekers with respect to their relevance to a job offer. To address this challenge, we propose an interactive system that follows the ``human-in-the-loop'' approach, actively involving recruiters in the job offer -- applicant CV matching. The system uses a fine-tuned state-of-the-art classification model that aligns job seeker CVs with labels of the {\it European Skills, Competences, Qualifications and Occupations} taxonomy to propose an initial match between job offers with the CVs of job candidates. This match is refined in sequential LLM driven-interaction with the recruiter, which culminates in CV relevance scores and reports that justify them.
Keywords:
Natural Language Processing: NLP: Applications
Humans and AI: HAI: Human-AI collaboration
Machine Learning: ML: Applications
Natural Language Processing: NLP: Language models
Natural Language Processing: NLP: Text classification