Selecting Between BERT and GPT for Text Classification in Political Science Research
This provides practical guidance for political science researchers dealing with limited labeled data, though it is incremental as it compares existing methods in a specific domain.
The study compared BERT fine-tuning and GPT-based prompt engineering for text classification in political science under data scarcity, finding that GPT models with zero-shot or few-shot learning are useful for early exploration but generally underperform or match BERT when training data reaches around 1,000 samples.
Political scientists often grapple with data scarcity in text classification. Recently, fine-tuned BERT models and their variants have gained traction as effective solutions to address this issue. In this study, we investigate the potential of GPT-based models combined with prompt engineering as a viable alternative. We conduct a series of experiments across various classification tasks, differing in the number of classes and complexity, to evaluate the effectiveness of BERT-based versus GPT-based models in low-data scenarios. Our findings indicate that while zero-shot and few-shot learning with GPT models provide reasonable performance and are well-suited for early-stage research exploration, they generally fall short - or, at best, match - the performance of BERT fine-tuning, particularly as the training set reaches a substantial size (e.g., 1,000 samples). We conclude by comparing these approaches in terms of performance, ease of use, and cost, providing practical guidance for researchers facing data limitations. Our results are particularly relevant for those engaged in quantitative text analysis in low-resource settings or with limited labeled data.