title: ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets
    and Large Language Models
summary: 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.
paper_authors._id: 667e73ec3b2efcc4414c055b
paper_authors.name: Ahmed Heakl
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-28T09:12:59.542Z
paper_authors._id: 656864e12d73834278a8dea7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/656864e12d73834278a8dea7/sfAWS2eyPtFHb_2GZIypp.jpeg
paper_authors.fullname: Ahmed Heakl
paper_authors.type: user
paper_authors.user: ahmedheakl
paper_authors._id: 667e73ec3b2efcc4414c055c
paper_authors.name: Youssef Mohamed
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-30T20:57:33.427Z
paper_authors._id: 646549e1fafbb0cdb409322e
paper_authors.avatarUrl: /avatars/d50714dc6723ac1a1e57e255dfb8a976.svg
paper_authors.fullname: Youssef Mohamed Khalil Mohamed
paper_authors.type: user
paper_authors.user: youssefkhalil320
paper_authors._id: 667e73ec3b2efcc4414c055d
paper_authors.name: Noran Mohamed
paper_authors._id: 667e73ec3b2efcc4414c055e
paper_authors.name: Ali Sharkaway
paper_authors._id: 667e73ec3b2efcc4414c055f
paper_authors.name: Ahmed Zaky
paper_title: ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets
    and Large Language Models
paper_summary: 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.
paper_ai_summary: A large-scale dataset and advanced large language models like BERT and Gemma1.1 2B improve resume classification accuracy and robustness in online recruitment.
paper_ai_keywords: Large Language Models BERT Gemma1.1 2B resume classification dataset quality accuracy robustness