Artificial Intelligence · 14.08.2026, 04:10 UTC
Class-Structure Preservation Beats Diversity: A Comprehensive Benchmark of Text Augmentation Methods for Imbalanced Text Classification
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | arXiv cs.CL ↗ |
| Veröffentlicht | 14.08.2026 UTC |
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arXiv:2608.12340v1 Announce Type: new Abstract: With the rapid advancement of large language models (LLMs), generative data augmentation has attracted considerable attention for imbalanced text classification in natural language processing. However, no empirical benchmark to date has compared LLM-based augmentation against the embedding-space SMOTE-style retrieval (EmbSMOTE), a strong classical reference for imbalanced classification. In this study, a controlled benchmark of 11 augmentation methods, spanning classical perturbation, embedding-space retrieval, and LLM-based generation, is newly constructed on seven public text classification datasets covering class counts $K=2$-$28$ and imbalance ratios of 1.1 to over 500, evaluated with five random seeds per cell using macro F1, Welch's $t$-tests, five distributional metrics, and an LLM-family sensitivity analysis based on Qwen3-8B. The experimental results reveal that all LLM-based methods are statistically equivalent or inferior to EmbSMOTE, with the performance gap widening monotonically as imbalance increases and reaching $\Delta\text{F1}_\text{macro}\!\approx\!0.063$ on GoEmotions-28. Furthermore, it is observed that surface-level uniqueness has negligible correlation with downstream performance, whereas LLM-specific artifacts, such as text elongation and label-distribution uniformization, are negatively associated with classification accuracy. Compared with six LLM-based and four classical augmentation baselines, these results demonstrate that the effective variable is not surface-level diversity but …