Optimizing Curriculum Vitae Concordance: A Comparative Examination of Classical Machine Learning Algorithms and Large Language Model Architectures

Abstract

Digital recruitment systems have revolutionized the hiring paradigm, imparting exceptional efficiencies and extending the reach for both employers and job seekers. This investigation scrutinized 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 such as GPT-4, GPT-3, and LLAMA adeptly process unstructured textual content, capturing nuanced language and context with greater precision. We evaluated these methodologies utilizing two datasets comprising resumes and job descriptions to ascertain their accuracy, efficiency, and scalability. Our results revealed that while conventional models excel at processing 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