Paper Key : IRJ************274
Author: Mayuri Baliram Gund,Divyahsree Shaileshrao Chavhan,Sourabh Sanjay Magar,Shreya Chandrakant Ghadage,Rohini Jadhav
Date Published: 17 Nov 2023
Abstract
In the contemporary job market, effective resume screening is a critical yet challenging HR task. Manual evaluation processes are labor-intensive and vulnerable to biases. This research introduces an innovative approach to resume shortlisting and ranking, leveraging advanced Natural Language Processing (NLP) techniques, with a specific emphasis on Large Language Models (LLMs), notably BERT. LLMs, with their contextual comprehension and text analysis capabilities, offer a robust solution for resume analysis. By harnessing BERT, this study aims to move beyond conventional keyword-based methods, ushering objectivity and efficiency into candidate assessment. The integrated approach in this research, encompassing data preprocessing to ranking algorithms, promises substantial advantages, including time savings, bias reduction, and improved candidate matching. Positioned at the intersection of LLMs and NLP, this research modernizes the recruitment process by tapping into the potential of BERT. Detailed methodology and results further demonstrate the transformative impact of LLMs and NLP in HR practices, underscoring the potential for data-driven decision-making and human expertise coexisting harmoniously in talent acquisition.
DOI LINK : 10.56726/IRJMETS45589 https://www.doi.org/10.56726/IRJMETS45589