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.
@article{arxiv.2403.04190,
title = {Generative AI for Synthetic Data Generation: Methods, Challenges and the Future},
author = {Xu Guo and Yiqiang Chen},
journal= {arXiv preprint arXiv:2403.04190},
year = {2024}
}