Abstract
This paper presents a condensed overview of TalentCLEF 2025, the first community evaluation initiative focused on job and skill intelligence in multilingual settings. The campaign attracted 15 participating teams and 280 system submissions from academia and industry across four continents. Analysis of methodological trends reveals a strong reliance on retrieval-based approaches, with selective integration of prompting, re-ranking, and external knowledge. This report highlights key participation insights and methodological patterns that may inform the design of future community challenges in natural language processing for labor market intelligence. TalentCLEF 2025 corpus: https://doi. org/10.5281/zenodo. 14002665
Type
Publication
CEUR Working Notes
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