Large Language Models for Text Classification: From Zero-Shot Learning to Instruction-Tuning

Abstract

Large language models (LLMs) have tremendous potential for social science research as they are trained on vast amounts of text and can generalize to many tasks. We explore the use of LLMs for supervised text classification, specifically the application to stance detection, which involves detecting attitudes and opinions in texts. We examine the performance of these models across different architectures, training regimes, and task specifications. We compare 10 models ranging in size from tens of millions to hundreds of billions of parameters and test four distinct training regimes: Prompt-based zero-shot learning and few-shot learning, fine-tuning, and instruction-tuning, which combines prompting and fine-tuning. The largest, most powerful models generally offer the best predictive performance even with little or no training examples, but fine-tuning smaller models is a competitive solution due to their relatively high accuracy and low cost. Instruction-tuning the latest generative LLMs expands the scope of text classification, enabling applications to more complex tasks than previously feasible. We offer practical recommendations on the use of LLMs for text classification in sociological research and discuss their limitations and challenges. Ultimately, LLMs can make text classification and other text analysis methods more accurate, accessible, and adaptable, opening new possibilities for computational social science.