Skill-LLM: Repurposing General-Purpose LLMs for Skill Extraction

• URL: http://arxiv.org/abs/2410.12052v1
• Date: Tue, 15 Oct 2024 20:41:18 GMT
• Title: Skill-LLM: Repurposing General-Purpose LLMs for Skill Extraction
• Authors: Amirhossein Herandi, Yitao Li, Zhanlin Liu, Ximin Hu, Xiao Cai,
• Abstract summary: We propose fine-tuning a specialized Skill-LLM and a light weight model to improve the precision and quality of skill extraction. Our results show that this approach outperforms existing SOTA techniques.
• Score: 2.5069344340760717
• License:
• Abstract: 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.

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