JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance
  Skill Matching
Antoine Magron Anna Dai Mike Zhang Syrielle Montariol Antoine Bosselut
Recent approaches in skill matching, employing synthetic training data for
classification or similarity model training, have shown promising results,
reducing the need for time-consuming and expensive annotations. However,
previous synthetic datasets have limitations, such as featuring only one skill
per sentence and generally comprising short sentences. In this paper, we
introduce JobSkape, a framework to generate synthetic data that tackles these
limitations, specifically designed to enhance skill-to-taxonomy matching.
Within this framework, we create SkillSkape, a comprehensive open-source
synthetic dataset of job postings tailored for skill-matching tasks. We
introduce several offline metrics that show that our dataset resembles
real-world data. Additionally, we present a multi-step pipeline for skill
extraction and matching tasks using large language models (LLMs), benchmarking
against known supervised methodologies. We outline that the downstream
evaluation results on real-world data can beat baselines, underscoring its
efficacy and adaptability.