Automating Resume Analysis: Knowledge Graphs via Prompt Engineering

The rapid digitization of recruitment processes and the growing complexity of resume data have posed significant challenges in managing and extracting information from such sources. Traditional methods necessitate innovative approaches that can adapt and scale effectively. This paper introduces a methodology employing Large Language Models (LLMs) facilitated by advanced prompt engineering techniques, to construct Knowledge Graphs (KGs) directly from resumes. Our approach bypasses the extensive customization typically required for domain-specific tasks, leveraging the intrinsic capabilities of LLMs to interpret and organize complex data. We evaluate our methodology, focusing particularly on Named Entity Recognition (NER) as a measure of effectiveness. The results demonstrate superior performance of our system against baseline models. Additionally, we explore the practical applicability of our system through a novel self-consistency metric, which further attests to the method’s ability to accurately capture and reproduce essential resume information in KG format. This study not only underscores the potential of LLMs in automated information extraction but also opens up new avenues for research and application in the HR technology domain and beyond.