Best Practices and Lessons Learned on Synthetic Data for Language Models
Ruibo Liu Jerry Wei Fangyu Liu Chenglei Si Yanzhe Zhang Jinmeng Rao Steven Zheng Daiyi Peng Diyi Yang Denny Zhou Andrew M. Dai
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.