  Supervised text models are a valuable tool for political scientists but
present several obstacles to their use, including the expense of hand-labeling
documents, the difficulty of retrieving rare relevant documents for annotation,
and copyright and privacy concerns involved in sharing annotated documents.
This article proposes a partial solution to these three issues, in the form of
controlled generation of synthetic text with large language models. I provide a
conceptual overview of text generation, guidance on when researchers should
prefer different techniques for generating synthetic text, a discussion of
ethics, and a simple technique for improving the quality of synthetic text. I
demonstrate the usefulness of synthetic text with three applications:
generating synthetic tweets describing the fighting in Ukraine, synthetic news
articles describing specified political events for training an event detection
system, and a multilingual corpus of populist manifesto statements for training
a sentence-level populism classifier.
