Version 1
: Received: 9 July 2024 / Approved: 10 July 2024 / Online: 10 July 2024 (09:57:41 CEST)
How to cite:
Maree, M.; Shehada, W. Optimizing Curriculum Vitae Concordance: A Comparative Examination of Classical Machine Learning Algorithms and Large Language Model Architectures. Preprints2024, 2024070828. https://doi.org/10.20944/preprints202407.0828.v1
Maree, M.; Shehada, W. Optimizing Curriculum Vitae Concordance: A Comparative Examination of Classical Machine Learning Algorithms and Large Language Model Architectures. Preprints 2024, 2024070828. https://doi.org/10.20944/preprints202407.0828.v1
Maree, M.; Shehada, W. Optimizing Curriculum Vitae Concordance: A Comparative Examination of Classical Machine Learning Algorithms and Large Language Model Architectures. Preprints2024, 2024070828. https://doi.org/10.20944/preprints202407.0828.v1
APA Style
Maree, M., & Shehada, W. (2024). Optimizing Curriculum Vitae Concordance: A Comparative Examination of Classical Machine Learning Algorithms and Large Language Model Architectures. Preprints. https://doi.org/10.20944/preprints202407.0828.v1
Chicago/Turabian Style
Maree, M. and Wala’a Shehada. 2024 "Optimizing Curriculum Vitae Concordance: A Comparative Examination of Classical Machine Learning Algorithms and Large Language Model Architectures" Preprints. https://doi.org/10.20944/preprints202407.0828.v1
Abstract
Digital recruitment systems have revolutionized the hiring paradigm, imparting exceptional efficiencies and extending the reach for both employers and job seekers. This investigation scrutinizes the efficacy of classical machine learning methodologies alongside advanced large language models (LLMs) in aligning resumes with job categories. Traditional matching techniques, such as Logistic Regression, Decision Trees, Naïve Bayes, and Support Vector Machines, are constrained by the necessity of manual feature extraction, limited feature representation, and performance degradation, particularly as dataset size escalates, rendering them less suitable for large-scale applications. Conversely, LLMs like GPT-4, GPT-3, and LLAMA, adeptly process unstructured textual content, capturing nuanced language and context with greater precision. We evaluate these methodologies utilizing two datasets comprising resumes and job descriptions to ascertain accuracy, efficiency, and scalability. Our results reveal that while conventional models excel with structured data, LLMs significantly enhance the interpretation and matching of intricate textual information. This study highlights the transformative potential of LLMs in recruitment, offering insights into their application and future research avenues.
Keywords
digital recruitment systems; classical machine learning; large language models (LLMs); performance degradation; methodology comparison; recruitment transformation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
