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.
@article{arxiv.2402.03242,
title = {JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching},
author = {Antoine Magron and Anna Dai and Mike Zhang and Syrielle Montariol and Antoine Bosselut},
journal= {arXiv preprint arXiv:2402.03242},
year = {2024}
}