Generative AI for Synthetic Data Generation: Methods, Challenges and the Future

Xu Guo, Yiqiang Chen
3/7/2024
cs.LGcs.AIcs.CL

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Abstract

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.

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Cite this paper

@article{guo2024generative,
  title  = {Generative AI for Synthetic Data Generation: Methods, Challenges and the Future},
  author = {Xu Guo and Yiqiang Chen},
  year   = {2024},
  eprint = {2403.04190},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.04190v1}
}

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