The field of machine learning has recently made significant progress in reducing the requirements for labelled training data when building new models. These `cheaper' learning techniques hold significant potential for the social sciences, where development of large labelled training datasets is often a significant practical impediment to the use of machine learning for analytical tasks. In this article we review three `cheap' techniques that have developed in recent years: weak supervision, transfer learning and prompt engineering. For the latter, we also review the particular case of zero-shot prompting of large language models. For each technique we provide a guide of how it works and demonstrate its application across six different realistic social science applications (two different tasks paired with three different dataset makeups). We show good performance for all techniques, and in particular we demonstrate how prompting of large language models can achieve high accuracy at very low cost. Our results are accompanied by a code repository to make it easy for others to duplicate our work and use it in their own research. Overall, our article is intended to stimulate further uptake of these techniques in the social sciences.

翻译：机器学习领域近期在减少构建新模型时对标注训练数据的需求方面取得了显著进展。这些“廉价”学习技术对社会学具有巨大潜力，因为在社会学中，开发大规模标注训练数据集通常是使用机器学习进行分析任务的主要实践障碍。本文综述了近年来发展的三种“廉价”技术：弱监督学习、迁移学习和提示工程。针对后者，我们还特别回顾了大语言模型的零样本提示案例。对于每种技术，我们提供了工作原理指南，并展示了其在六种不同的现实社会科学应用场景（两种任务搭配三种不同数据集构成）中的实际应用。所有技术均展现出良好性能，特别地，我们证明了对大语言模型进行提示能够以极低成本实现高准确率。我们的研究结果附带代码库，便于他人复现我们的工作并将其应用于自身研究。总体而言，本文旨在推动这些技术在社会学领域的进一步应用。