Multiclass Classification of Policy Documents with Large Language Models

Understand

Classifying policy documents into policy issue topics has been a long-time effort in political science and communication disciplines.

• Efforts to automate text classification processes for social science research purposes have so far achieved remarkable results, but there is still a large room for progress.
• In this work, we test the prediction performance of an alternative strategy, which requires human involvement much less than full manual coding.
• We use the GPT 3.5 and GPT 4 models of the OpenAI, which are pre-trained instruction-tuned Large Language Models (LLM), to classify congressional bills and congressional hearings into Comparative Agendas Project's 21 major policy issue topics.