mlsanityData to 7 Oct 2026

Utilizing Large Language Models for Information Extraction from Real Estate Transactions

arXiv 2404.18043 ↗First posted 28 Apr 2024cs.CL cs.IR cs.LGcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 16 of its 27 references that are in the map. First posted on arXiv 28 Apr 2024.

Builds on

16 of 27 references.

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4 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 0; 2026: 1.

7 papers.

  1. 2024Credit Card Fraud Detection Using Advanced Transformer Modelcited by 26
  2. 2024Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) datacited by 26
  3. 2024LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuningcited by 10
  4. 2024Deep Learning in Medical Image Classification from MRI-based Brain Tumor Imagescited by 7
  5. 2024Advancing Financial Risk Prediction Through Optimized LSTM Model Performance and Comparative Analysiscited by 5
  6. 2024A Survey of Retrieval Algorithms in Ad and Content Recommendation Systemscited by 5
  7. 2026Structured Data Extraction from Real Estate Documents using Clustering, Classification, and Large Language Modelscited by 0