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

TL;DR

This paper reviews how large language models are used to generate synthetic data, discussing methods, challenges, and future directions to improve AI training in low-resource scenarios.

Contribution

It provides a comprehensive overview of current techniques, evaluation methods, and practical applications of synthetic data generation using large language models.

Findings

LLMs can generate high-quality synthetic data for various tasks.

Current limitations include data quality and ethical concerns.

Future research should focus on improving realism and addressing biases.

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.