Utilizing Large Language Models for Information Extraction from Real Estate Transactions

Understand

Real estate sales contracts contain crucial information for property transactions, but manual data extraction 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.
• We generated synthetic contracts using the real-world transaction dataset, thereby fine-tuning the large-language model and achieving significant metrics improvements and qualitative improvements in information retrieval and reasoning tasks.