How to Use Large Language Models for Text Coding: The Case of Fatherhood Roles in Public Policy Documents.
CoRR(2023)
摘要
Recent advances in large language models (LLMs) like GPT-3 and GPT-4 have
opened up new op- portunities for text analysis in political science. They
promise automation with better results and less programming. In this study, we
evaluate LLMs on three original coding tasks of non-English political science
texts, and we provide a detailed description of a general workflow for using
LLMs for text coding in political science research. Our use case offers a
practical guide for researchers looking to incorporate LLMs into their research
on text analysis. We find that, when provided with detailed label definitions
and coding examples, an LLM can be as good as or even better than a human
annotator while being much faster (up to hundreds of times), considerably
cheaper (costing up to 60% less than human coding), and much easier to scale to
large amounts of text. Overall, LLMs present a viable option for most text
coding projects.
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