  The labor market is undergoing rapid changes, with increasing demands on job
seekers and a surge in job openings. Identifying essential skills and
competencies from job descriptions is challenging due to varying employer
requirements and the omission of key skills. This study addresses these
challenges by comparing traditional Named Entity Recognition (NER) methods
based on encoders with Large Language Models (LLMs) for extracting skills from
Russian job vacancies. Using a labeled dataset of 4,000 job vacancies for
training and 1,472 for testing, the performance of both approaches is
evaluated. Results indicate that traditional NER models, especially DeepPavlov
RuBERT NER tuned, outperform LLMs across various metrics including accuracy,
precision, recall, and inference time. The findings suggest that traditional
NER models provide more effective and efficient solutions for skill extraction,
enhancing job requirement clarity and aiding job seekers in aligning their
qualifications with employer expectations. This research contributes to the
field of natural language processing (NLP) and its application in the labor
market, particularly in non-English contexts.
