The work of our colleagues Bo, Nan and Tijl on Large Language Models (LLMs) for Named Entity Recognition (NER) applied to labour market data, has been featured on UGent@Work. The proposed model uses LLMs to automatically assign standard occupation codes to job vacancies based on their textual descriptions. In the post, Bo discusses some of the main features and benefits of the model, its social relevance as well as his thoughts on the future of occupational coding (the professional field dedicated to the manual mapping of job descriptions to standard taxonomies).
Below is a snippet of this post. You can read the rest on the ugentatwork blog.
🤖 Improve job matching with AI 🤖
In today’s dynamic labor market, it is becoming increasingly important for job seekers, recruiters and employment agencies to accurately match vacancies and candidate profiles. With LLM4Jobs, we’ve developed an AI tool that leverages the strengths of large language models to extract and standardize job-related data, ensuring precision and efficiency in matching job seekers with the right opportunities.