  Within the evolving landscape of deep learning, the dilemma of data quantity
and quality has been a long-standing problem. The recent advent of Large
Language Models (LLMs) offers a data-centric solution to alleviate the
limitations of real-world data with synthetic data generation. However, current
investigations into this field lack a unified framework and mostly stay on the
surface. Therefore, this paper provides an organization of relevant studies
based on a generic workflow of synthetic data generation. By doing so, we
highlight the gaps within existing research and outline prospective avenues for
future study. This work aims to shepherd the academic and industrial
communities towards deeper, more methodical inquiries into the capabilities and
applications of LLMs-driven synthetic data generation.
