Generative AI for Synthetic Data Generation: Methods, Challenges and the Future
This addresses data scarcity issues for low-resource scenarios in AI, but it is incremental as it reviews existing methods and challenges.
The paper tackles the problem of limited data availability by exploring the use of large language models (LLMs) to generate synthetic data, noting that this approach performs comparably to real-world data.
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.