Rethinking Skill Extraction in the Job Market Domain using Large Language Models

Skill Extraction involves identifying skills and qualifications mentioned in documents such as job postings and resumes. The task is commonly tackled by training supervised models using a sequence labeling approach with BIO tags. However, the reliance on manually annotated data limits the generalizability of such approaches. Moreover, the common BIO setting limits the ability of the models to capture complex skill patterns and handle ambiguous mentions. In this paper, we explore the use of in-context learning to overcome these challenges, …

annotated data bio cs.cl data documents domain extraction job job market labeling language language models large language large language models reliance resumes skills tags training