Using Large Language Models for Reliable Evidence in International Research

Large language models are transforming how researchers analyze text. They can help classify political speeches, identify policy positions, detect misinformation, and process large collections of documents across languages and contexts. Yet they also raise an important question for social science and policy research: when machine-generated classifications are imperfect, how can we ensure that the conclusions drawn from them remain valid? Even when a model has high accuracy, above 90 percent, its remaining errors may not be random and can still bias research findings. To address this challenge,