Using Large Language Models in Real Estate Transactions: A Few-shot Learning Approach

Large language models (LLMs) have demonstrated significant potential in transforming real estate transactions through advanced natural language processing capabilities. This paper proposes a method leveraging LLMs to extract information from real estate contracts and facilitate interactive querying by users. By emphasizing the use of few-shot learning, LLMs can effectively analyze contract documents and respond to specific questions from users regarding contract details. This paper explores the development of this method, its implementation in real estate workflows, and evaluates its effectiveness in improving efficiency and accuracy within the industry.