Examinando por Materia "Talent.com"

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    Inteligencia del mercado laboral colombiano : detección automatizada de habilidades mediante modelos grandes de lenguaje (LLM) y recuperación aumentada (RAG)
    (Universidad EAFIT, 2025-12-03) Zapata Posada, Jorge Mario; Álvarez Barrera, Claudia Patricia; Padilla Buritica, Jorge Iván
    The demand for skills in the labor market has evolved significantly in recent decades, driven by changes in the economic environment and constant technological advances. In this context, the detailed description of each job offer, available on employment web portals, provides accurate information on the specific skills required by the market in real time. Labor Market Intelligence (LMI) research uses this data along with machine learning algorithms to anticipate trends and understand the evolution of talent demand. Despite advances in artificial intelligence and the availability of large data volumes, there remains a gap in adapting these technologies to local contexts. Regional markets, such as Colombia, require customized approaches to ensure that technological solutions respond to the specific needs of the labor market, effectively aligning talent supply and demand. This study analyzes data from the Talent.com employment platform for Colombia using a state-of-the-art approach based on Large Language Models (LLM) combined with Retrieval Augmented Generation (RAG) to identify emerging, traditional, technical, and soft skills. In the first stage, a multilingual LLM extracts skill mentions from job descriptions. In the second stage, a semantic retrieval module queries the European Commission’s open ESCO skills taxonomy to propose standardized candidate labels, the LLM then selects the most appropriate label and delivers validated, structured JSON outputs. Preliminary results show improvements in precision, coverage, and auditability compared to purely supervised approaches, reducing hallucinations through candidate-constrained selection and standardizing categories using ESCO skill classification. This framework provides valuable insights that, in future work, may support universities in designing academic programs aligned with labor market needs, thus facilitating strategic decision-making for employers, policymakers, and educators, and contributing to talent development and the reduction of unemployment in Colombia.