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Recent advances in large language models (LLMs) like GPT-3 and GPT-4 haveopened up new opportunities for text analysis in political science. Theypromise automation with better results and less programming. In this study, weevaluate LLMs on three original coding tasks of non-English political sciencetexts, and we provide a detailed description of a general workflow for usingLLMs for text coding in political science research. Our use case offers apractical guide for researchers looking to incorporate LLMs into their researchon text analysis. We find that, when provided with detailed label definitionsand coding examples, an LLM can be as good as or even better than a humanannotator while being much faster (up to hundreds of times), considerablycheaper (costing up to 60% less than human coding), and much easier to scale tolarge amounts of text. Overall, LLMs present a viable option for most textcoding projects.

How to Use Large Language Models for Text Coding: The Case of Fatherhood Roles in Public Policy Documents