career path recommendation using large language models

In the contemporary job market, efficient and precise resume analysis is essential for high-level tasks such as career path recommendations and job matching. This work introduces a novel approach to streamlining the process by harnessing the power of large language models. The primary objective is to develop a comprehensive pipeline for information extraction, data mapping, and subsequent utilization in downstream recommendation and matching tasks. Resumes are often parsed from different formats, resulting in unstructured and noisy text that poses a significant challenge for automated analysis. We aim to enhance the accuracy and efficacy of information extraction by leveraging recent developments in natural language processing, particularly large language models. The proposed pipeline begins with an information extraction module that efficiently retrieves key data points, including skills, qualifications, and experiences. Subsequently, a data mapping component maps different sections, such as job titles, places, and degrees, to a smaller taxonomy. The extracted information is then employed in downstream tasks, such as career path recommendations and job matching. For information extraction, we propose different methods, such as prompt engineering and LoRa. For data mapping, we experimented with fine-tuning and heuristic methods to increase accuracy. We evaluate the proposed pipeline using a rich dataset provided by VDAB, encompassing resumes in Dutch, French, and English. We further enhanced this dataset by annotating it to facilitate resume analysis and information extraction.