Accurate skill extraction from job descriptions is crucial in the hiring process but remains challenging. Named Entity Recognition (NER) is a common approach used to address this issue. With the demonstrated success of large language models (LLMs) in various NLP tasks, including NER, we propose fine-tuning a specialized Skill-LLM and a light weight model to improve the precision and quality of skill extraction. In our study, we evaluated the fine-tuned Skill-LLM and the light weight model using a benchmark dataset and compared its performance against state-of-the-art (SOTA) methods. Our results show that this approach outperforms existing SOTA techniques.
@article{arxiv.2410.12052,
title = {Skill-LLM: Repurposing General-Purpose LLMs for Skill Extraction},
author = {Amirhossein Herandi and Yitao Li and Zhanlin Liu and Ximin Hu and Xiao Cai},
journal= {arXiv preprint arXiv:2410.12052},
year = {2024}
}