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

TL;DR

This study evaluated the fine-tuned Skill-LLM and the light weight model and compared its performance against state-of-the-art (SOTA) methods and showed that this approach outperforms existing SOTA techniques.

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.

Figures (4)

• Figure 1: Example for fine tuned LLM input and output formats
• Figure 2: Example for Extract-Style LLM prompting
• Figure 3: Example for NER-Style LLM prompting
• Figure 4: Example for GLiNER data format