Large language models (LLMs) have shown promise in text classification, but their effectiveness depends on the availability of large labeled datasets. This paper reviews the potential and limitations of using LLMs for text classification through synthetic data generation. It explores methodologies like data augmentation, adversarial training, and transfer learning to address data scarcity and domain adaptation. The paper examines the effectiveness of these approaches in enhancing classification performance, discusses challenges (data privacy, bias amplification, model fairness), and analyzes the impact of model size, pretraining data, and fine-tuning strategies. Finally, it identifies key research gaps and proposes future directions, including developing better evaluation metrics for synthetic data and investigating long-term societal impacts.

Ashok Kumar Pamidi Venkata, Leeladhar Gudala