The method of improving advertising texts based on the use of generative models

Abstract

The article is dedicated to the development of a new method of improvement advertising texts based on the use of generative models. Advertising content plays a crucial role in modern marketing, as it fosters a brand's interaction with the audience and attracts new customers. However, creating effective advertising content often presents a challenge. Generative models open up new opportunities for creating advertising texts. They can be used to automate the process of content creation, providing a high level of originality and creativity. Specifically, this article focuses on the use of GPT series models for generating advertising texts. The first task within this research is to study the theoretical basis of generative models and their capabilities for text creation. The authors conduct a detailed analysis of the main principles of these models' operation, as well as their potential use in the context of advertising text. Further, the article describes a method of collecting and preparing input data for training generative models. Since the quality of output texts heavily depends on the quality of input data, this stage is important for the successful application of generative models. Next, the authors develop an algorithm for training the generative model based on the collected data. They describe the process of selecting optimal hyperparameters for the model, which is vital to achieve the best results. The approach presented in this article is a significant contribution to developing new methods for optimizing advertising texts. It has considerable potential for use in the marketing sphere, where there is a need to quickly and effectively generate large volumes of content. At the same time, the research results may be useful for further scientific studies in this field