Abstract:
Recruitment agencies and firms encounter a significant challenge in handling the mul-
titude of resumes they receive daily, which come in various formats such as PDFs, DOCX
files, and with diverse layouts. Extracting relevant information and identifying suitable can-
didates from this diverse pool is a time-consuming process due to the resumes’ unstructured
nature and variations in format, style, and content. To address this challenge, a system has
been proposed to automate the conversion of unstructured resumes into standard structured
formats. The system aims to generate resumes in a standard template, ensuring uniformity
across all resumes. This automation process is crucial for streamlining the resume screen-
ing process, saving time and effort for recruiters. The proposed system utilizes the qlora
parameter-efficient fine-tuning technique with the Llama 2 model. This technique minimizes
the need for extensive GPU resources while achieving effective fine-tuning. The fine-tuned
model yielded an impressive F1 score of 0.8928, surpassing the performance of the previously
instruction-tuned model. Overall, the proposed system offers a robust solution for automat-
ing the resume screening process. By improving the efficiency and effectiveness of candidate
selection, it provides significant benefits to recruitment agencies and firms, allowing them to
focus on more strategic tasks.
