Utilizing Large Language Models for Information Extraction from Real Estate Transactions
This work addresses efficiency and accuracy issues in real estate contract analysis, but it is incremental as it applies existing transformer-based methods to a specific domain.
The paper tackled the problem of manual data extraction from real estate sales contracts by applying large language models, achieving significant improvements in metrics and qualitative performance for information retrieval and reasoning tasks.
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.