Summary of Resumeatlas: Revisiting Resume Classification with Large-scale Datasets and Large Language Models, by Ahmed Heakl et al.
ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets and Large Language Models
by Ahmed Heakl, Youssef Mohamed, Noran Mohamed, Aly Elsharkawy, Ahmed Zaky
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to efficient resume classification is proposed, tackling challenges posed by small datasets, varying templates, and privacy concerns. A comprehensive methodology is presented, utilizing Large Language Models (LLMs) like BERT and Gemma1.1 2B to classify resumes. The approach yields significant improvements over traditional machine learning methods, with a top-1 accuracy of 92% and top-5 accuracy of 97.5%. This highlights the importance of high-quality datasets and advanced model architectures in enhancing resume classification systems, thus advancing online recruitment practices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Online recruitment platforms rely heavily on AI technologies. However, small datasets, varying templates, and privacy concerns hinder the accuracy of existing resume classification models. A new approach addresses these challenges by curating a large-scale dataset of 13,389 resumes from diverse sources and using advanced machine learning models like BERT and Gemma1.1 2B for classification. The results show that this method is more accurate than traditional approaches, with an accuracy of 92% and 97.5%. This means the approach can better help people find jobs. |
Keywords
» Artificial intelligence » Bert » Classification » Machine learning
