Diffusion-Based Tabular Generative Models Outperform CTGAN in LLM Data Augmentation for Imbalanced Text Classification

This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How does the F1-score of diffusion-based tabular generative models compare to CTGAN when augmenting data for training LLMs on imbalanced text classification benchmarks. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the F1-score of diffusion-based tabular generative models compare to CTGAN when augmenting data for training LLMs on imbalanced text classification benchmarks?

Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.