Class-Structure Preservation Beats Diversity: A Comprehensive Benchmark of Text Augmentation Methods for Imbalanced Text Classification
The hosts discuss a benchmark paper showing that simple, retrieval-based text augmentation methods outperform complex LLM generation techniques for imbalanced text classification. The authors argue that preserving the structural integrity of a data class is more important than creating superficially diverse sentences.