Summary of Utilizing Large Language Models For Information Extraction From Real Estate Transactions, by Yu Zhao et al.
Utilizing Large Language Models for Information Extraction from Real Estate Transactions
by Yu Zhao, Haoxiang Gao
First submitted to arxiv on: 28 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the application of transformer-based large language models for automated information extraction from real estate sales contracts. The authors discuss challenges, techniques, and future directions in leveraging these models to improve efficiency and accuracy in real estate contract analysis. They fine-tune a model on synthetic contracts generated using a real-world transaction dataset, achieving significant improvements in metrics and qualitative tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computer models called “large language models” to help analyze important information in real estate sales contracts. These models are like super-smart assistants that can quickly find key details in documents. The authors make these models better by teaching them on fake contracts, which helps them learn to be more accurate and efficient. This can help people working with real estate contracts do their jobs faster and with fewer mistakes. |
Keywords
» Artificial intelligence » Transformer
