  Real estate sales contracts contain crucial information for property
transactions, but manual extraction of data can be time-consuming and
error-prone. This paper explores the application of large language models,
specifically transformer-based architectures, for automated information
extraction from real estate contracts. We discuss challenges, techniques, and
future directions in leveraging these models to improve efficiency and accuracy
in real estate contract analysis.
