JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching
This work addresses the need for better synthetic training data in skill matching for job posting analysis, though it is incremental as it builds on existing synthetic data approaches.
The authors tackled the problem of limited synthetic job posting data for skill matching by introducing JobSkape, a framework that generates more realistic synthetic data, and SkillSkape, an open-source dataset, which improved downstream skill extraction and matching tasks, beating baselines on real-world evaluations.
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.