Best Practices and Lessons Learned on Synthetic Data
This is an incremental review paper that addresses data scarcity, privacy concerns, and high costs in AI development for researchers and practitioners.
The paper tackles the challenge of obtaining large, diverse, and high-quality datasets for AI models by providing an overview of synthetic data research, discussing its applications, challenges, and future directions, and presenting empirical evidence to demonstrate its effectiveness.
The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and high costs. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns. This paper provides an overview of synthetic data research, discussing its applications, challenges, and future directions. We present empirical evidence from prior art to demonstrate its effectiveness and highlight the importance of ensuring its factuality, fidelity, and unbiasedness. We emphasize the need for responsible use of synthetic data to build more powerful, inclusive, and trustworthy language models.