Optimizing Resume Parsing Processes by Leveraging Large Language Models

In the present era of internet revolution, organizations have to go through numerous resumes in order to identify the most suitable candidates for their Job Description (JD), while ensuring that no acquisition of talent is overlooked through human mistakes. Thus, tools like Applicant Tracking Systems (ATS) have taken over the human process of resume screening, enabling assessment of thousands of resumes in a matter of seconds. Although these technologies are incredibly effective, they are not flawless. Consequently, highly qualified individuals may miss out on said opportunities if their resumes are not formatted correctly. Therefore, individuals must ensure that their resume is appropriately structured prior to submitting it to any organization. The study centers on the present literature and suggests an enhanced approach for parsing resumes by leveraging Large Language Models (LLMs).