Automated Data Extraction from Unstructured Text Using LLMs

This technical report presents a scalable workflow for extracting data from unstructured text using large language models (LLMs), specifically for Stata users. It begins by outlining the necessity of data extraction, noting that over 80% of data is unstructured and that traditional methods of extraction are often labor-intensive and limited in adaptability. The report details various traditional methods, including rule-based systems, classical machine learning, and early deep learning approaches, highlighting their respective limitations. It transitions to discussing LLMs, explaining their architecture, including self-attention and multi-head attention mechanisms, and how they learn through loss functions and reinforcement learning. Furthermore, the report describes various prompting strategies for data extraction, emphasizing the importance of prompt quality. It concludes with a discussion of the performance of LLMs in extracting structured data across various domains, including clinical and health data, and addresses key parameters to control during the extraction process, as well as risks and limitations associated with LLMs.