Utilizing Large Language Models for Information Extraction from Real Estate Transactions

2024-04-28

arXiv.org

AI Summary

To address the time-consuming and error-prone nature of manual key information extraction from real estate sales contracts, this paper proposes a domain-specific large language model (LLM)-based information extraction method. The approach leverages high-quality synthetic contracts—generated for the first time from real transaction data—to supervise fine-tuning of Transformer-based LLMs, integrated with domain adaptation techniques. This enhances the model’s capability in structured information retrieval and logical reasoning over complex contractual clauses and heterogeneous document formats. Experimental results demonstrate an average 12.3% improvement in F1 score across multiple critical field extraction tasks. Qualitative analysis confirms the method’s robustness and practical utility on real-world contracts. The proposed framework establishes a reusable technical pathway for intelligent parsing of legal and financial documents.

Technology Category

• Natural Language Processing: Information Extraction
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Automating extraction of real estate contract data to reduce manual errors
• Applying transformer models to improve efficiency in contract analysis
• Enhancing information retrieval accuracy using synthetic training data

Innovation

• Transformer-based large language models for extraction
• Synthetic contracts for fine-tuning models
• Improved accuracy in real estate analysis