JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance
Skill Matching
- URL: http://arxiv.org/abs/2402.03242v1
- Date: Mon, 5 Feb 2024 17:57:26 GMT
- Title: JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance
Skill Matching
- Authors: Antoine Magron, Anna Dai, Mike Zhang, Syrielle Montariol, Antoine
Bosselut
- Abstract summary: JobSkape is a framework to generate synthetic data for skill-to-taxonomy matching.
Within this framework, we create SkillSkape, a comprehensive open-source synthetic dataset of job postings.
We present a multi-step pipeline for skill extraction and matching tasks using large language models.
- Score: 18.94748873243611
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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