The development of large language models (LLMs) with each new iteration demonstrates a significant improvement in their ability to understand and generate text, which opens up increasingly wide opportunities for their integration into information processing systems and digital business processes of enterprises and institutions.
The article examines approaches to formatting tabular data (HTML, XML, Markdown, CSV) for the subsequent generation of synthetic samples using large language models (LLM) in predictive monitoring tasks. Since real-world data are often characterized by class imbalance, generating additional samples helps improve training datasets, thereby enhancing the effectiveness of models. At the same time, an important issue arises regarding processing speed and query costs, which largely depend on how many input tokens are required by the chosen format for tabular data representation.