Best Practices and Lessons Learned on Synthetic Data for Language Models

arXiv:2404.07503v1 Announce Type: new
Abstract: 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 …

abstract ai models artificial arxiv availability best practices concerns costs cs.cl data datasets diverse language language models lessons learned practices privacy quality solution success synthetic synthetic data type