ABSTRACT This article explores the use of large language models (LLMs), specifically GPT, for enhancing information extraction from unstructured text in political science research. By automating the retrieval of explicit details from sources including historical documents, meeting minutes, news articles, and unstructured search results, GPT significantly reduces the time and resources required for data collection. The study highlights how GPT complements human research assistants, combining automated efficiency with human oversight to improve the reliability and depth of research. This integration not only makes comprehensive data collection more accessible; it also increases the overall research efficiency and scope of research. The article highlights GPT’s unique capabilities in information extraction and its potential to advance empirical research in the field. Additionally, we discuss ethical concerns related to student employment, privacy, bias, and environmental impact associated with the use of LLMs.
Lee et al. (Wed,) studied this question.