Resume Optimisation and Suggestions using Large Language Models: A Review

Abstract

Abstract: In the rapidly evolving job market, the compatibility of a candidate's resume with the job description is critical to the application process. Conventional resume optimization techniques frequently depend on keyword matching, which is shallow and cannot comprehend linguistic nuances or context, resulting in an inadequate match between applicant's profiles and job criteria. This study presents a novel application that uses Bidirectional Encoder Representations from Transformers (BERT) to generate language embeddings from job descriptions and resumes, therefore bridging this gap. Our program offers a contextaware and nuanced evaluation of how well a candidate's qualifications match the requirements of the position by calculating the similarity scores between these embeddings. Furthermore, the incorporation of Generative Pre-trained Transformer (GPT) models provides customized suggestions for resume optimization, emphasizing areas of improvement indicated by the BERT study. Additionally, the application also has a job search tool that lets the users browse LinkedIn for suitable job posts and apply straight to them.