Deep Learning-Driven Labor Education and Skill Assessment: A Big Data Approach for Optimizing Workforce Development and Industrial Relations

The automation of resume screening is a critical component of modern recruitment processes, particularly in large organizations. Automated systems for resume screening typically involve various NLP tasks to streamline candidate evaluation. This paper investigates the application of LLM models in automating labor education and skill assessment, focusing on optimizing workforce development through advanced language models. We propose a comprehensive framework for automating resume screening and grading, utilizing SOTA LLM models to enhance recruitment processes. The proposed system integrates information extraction and summarization tasks, leveraging LLMs for decision-making throughout the hiring process. Our experiments, conducted on a publicly available resume dataset, demonstrate significant improvements in efficiency and accuracy. The LLaMA2-13B model, achieves a ROUGE-1 score of 37.31, ROUGE-2 of 15.04, ROUGE-L of 36.99, and BLEU score of 13.82, significantly outperforming the baseline models such as FLAN-T5 and GPT-NeoX. These results highlight the potential of LLM-based systems in automating labor-related assessments, with the fine-tuned LLaMA2-13B model delivering up to 27% better performance than zero-shot models.