ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets and Large Language Models
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916321868906496 |
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| author | Heakl, Ahmed Mohamed, Youssef Mohamed, Noran Elsharkawy, Aly Zaky, Ahmed |
| author_facet | Heakl, Ahmed Mohamed, Youssef Mohamed, Noran Elsharkawy, Aly Zaky, Ahmed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18125 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets and Large Language Models Heakl, Ahmed Mohamed, Youssef Mohamed, Noran Elsharkawy, Aly Zaky, Ahmed Computation and Language Artificial Intelligence Computers and Society Machine Learning 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. |
| title | ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets and Large Language Models |
| topic | Computation and Language Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2406.18125 |