Extreme Multi-Label Skill Extraction Training using Large Language
  Models
Jens-Joris Decorte Severine Verlinden Jeroen Van Hautte Johannes Deleu Chris Develder Thomas Demeester
Online job ads serve as a valuable source of information for skill
requirements, playing a crucial role in labor market analysis and e-recruitment
processes. Since such ads are typically formatted in free text, natural
language processing (NLP) technologies are required to automatically process
them. We specifically focus on the task of detecting skills (mentioned
literally, or implicitly described) and linking them to a large skill ontology,
making it a challenging case of extreme multi-label classification (XMLC).
Given that there is no sizable labeled (training) dataset are available for
this specific XMLC task, we propose techniques to leverage general Large
Language Models (LLMs). We describe a cost-effective approach to generate an
accurate, fully synthetic labeled dataset for skill extraction, and present a
contrastive learning strategy that proves effective in the task. Our results
across three skill extraction benchmarks show a consistent increase of between
15 to 25 percentage points in \textit{R-Precision@5} compared to previously
published results that relied solely on distant supervision through literal
matches.