# Utilizing Large Language Models for Information Extraction from Real Estate Transactions

arXiv: https://arxiv.org/abs/2404.18043 · HTML: https://mlsanity.com/paper/2404.18043/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 28 Apr 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks](https://mlsanity.com/paper/2106.04489/) (2021)
- [Rethinking and Improving Relative Position Encoding for Vision Transformer](https://mlsanity.com/paper/2107.14222/) (2021)
- [Best Practices and Lessons Learned on Synthetic Data](https://mlsanity.com/paper/2404.07503/) (2024)
- [A Survey for Foundation Models in Autonomous Driving](https://mlsanity.com/paper/2402.01105/) (2024)
- [Task Adaptive Parameter Sharing for Multi-Task Learning](https://mlsanity.com/paper/2203.16708/) (2022)
- [IMHO Fine-Tuning Improves Claim Detection](https://mlsanity.com/paper/1905.07000/) (2019)

## Built on by (most cited first, 8 of 8)

- [Credit Card Fraud Detection Using Advanced Transformer Model](https://mlsanity.com/paper/2406.03733/) (2024)
- [Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) data](https://mlsanity.com/paper/2407.00028/) (2024)
- [LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning](https://mlsanity.com/paper/2411.09947/) (2024)
- [Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images](https://mlsanity.com/paper/2408.00636/) (2024)
- [Advancing Financial Risk Prediction Through Optimized LSTM Model Performance and Comparative Analysis](https://mlsanity.com/paper/2405.20603/) (2024)
- [A Survey of Retrieval Algorithms in Ad and Content Recommendation Systems](https://mlsanity.com/paper/2407.01712/) (2024)
- [Optimized Coordination Strategy for Multi-Aerospace Systems in Pick-and-Place Tasks By Deep Neural Network](https://mlsanity.com/paper/2412.09877/) (2024)
- [Structured Data Extraction from Real Estate Documents using Clustering, Classification, and Large Language Models](https://mlsanity.com/paper/2607.06012/) (2026)

## 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.