Selecting Between BERT and GPT for Text Classification in Political Science Research

TL;DR

This paper compares BERT fine-tuning and GPT prompt-based methods for text classification in political science, especially under low-data conditions, providing practical guidance for researchers with limited labeled data.

Contribution

It systematically evaluates GPT-based prompt methods against BERT fine-tuning across various classification tasks in low-resource scenarios, highlighting their relative strengths and limitations.

Findings

GPT models perform reasonably in zero- and few-shot settings.

BERT fine-tuning outperforms GPT as data size increases.

Prompt engineering offers a cost-effective alternative for early-stage research.

Abstract

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.