Rethinking Skill Extraction in the Job Market Domain using Large Language Models
This work addresses skill extraction for job market applications, but it is incremental as it adapts existing LLM methods to a specific domain.
The paper tackled the problem of skill extraction from job market documents by using large language models (LLMs) with in-context learning, showing that LLMs better handle syntactically complex skill mentions compared to traditional supervised models, though they do not match their performance.
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, on a benchmark of 6 uniformized skill extraction datasets. Our approach leverages the few-shot learning capabilities of large language models (LLMs) to identify and extract skills from sentences. We show that LLMs, despite not being on par with traditional supervised models in terms of performance, can better handle syntactically complex skill mentions in skill extraction tasks.