The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks
This method helps applied researchers balance datasets for better performance in classification tasks, though it is incremental as it adapts existing generative LLMs for data augmentation.
The paper tackles the problem of insufficient training examples for underrepresented categories in supervised classification tasks by proposing the synthetic imputation approach, which uses GPT-4o to generate synthetic texts from five original examples, achieving performance on par with a full sample of original texts when 75 or more original examples are available.
Encoder-decoder Large Language Models (LLMs), such as BERT and RoBERTa, require that all categories in an annotation task be sufficiently represented in the training data for optimal performance. However, it is often difficult to find sufficient examples for all categories in a task when building a high-quality training set. In this article, I describe this problem and propose a solution, the synthetic imputation approach. Leveraging a generative LLM (GPT-4o), this approach generates synthetic texts based on careful prompting and five original examples drawn randomly with replacement from the sample. This approach ensures that new synthetic texts are sufficiently different from the original texts to reduce overfitting, but retain the underlying substantive meaning of the examples to maximize out-of-sample performance. With 75 original examples or more, synthetic imputation's performance is on par with a full sample of original texts, and overfitting remains low, predictable and correctable with 50 original samples. The synthetic imputation approach provides a novel role for generative LLMs in research and allows applied researchers to balance their datasets for best performance.