title: Best Practices and Lessons Learned on Synthetic Data for Language Models
summary: 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.
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paper_authors.name: Steven Zheng
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paper_authors.name: Diyi Yang
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paper_title: Best Practices and Lessons Learned on Synthetic Data for Language Models
paper_summary: 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.