The recent developments in the field of large language models (LLMs) and their applications in data synthesis and augmentation have shown significant advancements. Researchers are increasingly focusing on optimizing LLMs for specific tasks, such as educational tutoring, data generation, and personalized information retrieval, by fine-tuning these models on specialized datasets. Notably, there is a growing emphasis on developing cost-effective solutions that leverage smaller, more efficient models without compromising performance. Additionally, the integration of diffusion models and autoregressive techniques into tabular data generation has opened new avenues for handling heterogeneous data types and improving the realism of synthetic data. The field is also witnessing innovative approaches to controlling and enhancing the capabilities of black-box LLMs through the use of lightweight white-box controllers. Furthermore, the importance of data weighting and quality assessment in synthetic data generation is being re-evaluated to ensure that LLM-generated data aligns with real-world distributions, thereby enhancing the robustness of downstream applications. Overall, the trend is towards more specialized, efficient, and controllable LLM applications that address specific challenges in various domains.