THE ALGORITHM FOR GENERATING A PROFESSION PROFILE

This paper presents an innovative approach that combines text topic modeling and text generation techniques using large language models. The use of the latent Dirichlet allocation method makes it possible to effectively identify hidden thematic structures in text data arrays associated with various professions. These topic structures are then used as the basis for a large language model that performs the task of ranking and generating a list of the most relevant and promising professions. This approach not only improves the accuracy of identifying professional areas, but also contributes to a deeper understanding of labor market dynamics.