Generative AI for Synthetic Data Generation: Methods, Challenges and the Future
<|reference_start|>Generative AI for Synthetic Data Generation: Methods, Challenges and the Future: 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.<|reference_end|>
@article{guo2024generative,
    title={Generative AI for Synthetic Data Generation: Methods, Challenges and the
  Future},
    author={Xu Guo, Yiqiang Chen},
    journal={arXiv preprint arXiv:2403.04190},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04190},
    primaryClass={cs.LG cs.AI cs.CL}
}