Synthetically generated text for supervised text analysis
arXiv:2303.16028 · doi:10.1017/pan.2024.31
Abstract
Supervised text models are a valuable tool for political scientists but present several obstacles to their use, including the expense of hand-labeling documents, the difficulty of retrieving rare relevant documents for annotation, and copyright and privacy concerns involved in sharing annotated documents. This article proposes a partial solution to these three issues, in the form of controlled generation of synthetic text with large language models. I provide a conceptual overview of text generation, guidance on when researchers should prefer different techniques for generating synthetic text, a discussion of ethics, and a simple technique for improving the quality of synthetic text. I demonstrate the usefulness of synthetic text with three applications: generating synthetic tweets describing the fighting in Ukraine, synthetic news articles describing specified political events for training an event detection system, and a multilingual corpus of populist manifesto statements for training a sentence-level populism classifier.
References in corpus (8)
- Semantics derived automatically from language corpora contain human-like biases
- ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks
- Out of One, Many: Using Language Models to Simulate Human Samples
- The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
- Generating Training Data with Language Models: Towards Zero-Shot Language Understanding
- Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
- Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models
- Towards Coding Social Science Datasets with Language Models