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This paper benchmarks 11 text augmentation methods, including classical, embedding-space, and LLM-based approaches, across 7 imbalanced classification datasets. It finds that retrieval-based oversampling (EmbSMOTE) outperforms LLM-based augmentation, and that preserving class-conditional structure matters more than surface-level diversity.