On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey
It addresses the data quantity and quality problem for the academic and industrial communities, but is incremental as it synthesizes existing work.
This survey tackles the lack of a unified framework in LLMs-driven synthetic data generation by organizing studies based on a generic workflow, highlighting research gaps and outlining future directions.
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.