Generative AI for Synthetic Data Generation: Methods, Challenges and the
  Future
Xu Guo Yiqiang Chen
The recent surge in research focused on generating synthetic data from large
language models (LLMs), especially for scenarios with limited data
availability, marks a notable shift in Generative Artificial Intelligence (AI).
Their ability to perform comparably to real-world data positions this approach
as a compelling solution to low-resource challenges. This paper delves into
advanced technologies that leverage these gigantic LLMs for the generation of
task-specific training data. We outline methodologies, evaluation techniques,
and practical applications, discuss the current limitations, and suggest
potential pathways for future research.