ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets
  and Large Language Models
Authors: name: Ahmed Heakl, name: Youssef Mohamed, name: Noran Mohamed, name: Ali Sharkaway, name: Ahmed Zaky

The increasing reliance on online recruitment platforms coupled with the
adoption of AI technologies has highlighted the critical need for efficient
resume classification methods. However, challenges such as small datasets, lack
of standardized resume templates, and privacy concerns hinder the accuracy and
effectiveness of existing classification models. In this work, we address these
challenges by presenting a comprehensive approach to resume classification. We
curated a large-scale dataset of 13,389 resumes from diverse sources and
employed Large Language Models (LLMs) such as BERT and Gemma1.1 2B for
classification. Our results demonstrate significant improvements over
traditional machine learning approaches, with our best model achieving a top-1
accuracy of 92\% and a top-5 accuracy of 97.5\%. These findings underscore the
importance of dataset quality and advanced model architectures in enhancing the
accuracy and robustness of resume classification systems, thus advancing the
field of online recruitment practices.