Empirical Evaluation of Large Language Models in Resume Classification

The study’s primary objective is to investigate the effectiveness of Large Language Models (LLMs) in the specialized field of resume classification, a critical aspect of talent acquisition and human resource management. The research aims to overcome the limitations of conventional approaches that often rely on basic Natural Language Processing (NLP) techniques by introducing a more efficient alternative through the utilization of LLMs. In pursuit of this goal, a comprehensive empirical assessment was undertaken, encompassing multiple LLMs, including various iterations of Text Davinci and GPT models. The research methodology employed was rigorous, incorporating data preprocessing and text normalization techniques to ensure the robustness and credibility of the results. The study’s outcomes include a comparative analysis of the chosen LLMs, with a focus on essential performance metrics such as accuracy, precision, recall, and the F1-Score. The results obtained from this analysis demonstrate a significant enhancement in the performance of LLMs compared to traditional methods in the context of resume classification. In conclusion, this research provides invaluable insights into the applicability and effectiveness of LLMs within the realm of resume classification. It not only addresses existing limitations but also serves as a foundational work that paves the way for future research in this domain. Moreover, it underscores the transformative potential of LLMs in reshaping the landscape of talent acquisition and human resource management processes.