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.

人工智能模型的成功依赖于大规模、多样化、高质量的数据集的可用性，而由于数据稀缺、隐私问题和高成本，获取这些数据集可能具有挑战性。合成数据作为一种有前景的解决方案出现，通过生成模仿真实世界模式的人工数据。本文提供了合成数据研究的概述，并讨论了其应用、挑战和未来方向。我们通过先前研究中的实证证据来展示其有效性，并强调确保其真实性、保真性和无偏性的重要性。我们强调了在使用合成数据构建更强大、包容性和可信赖的语言模型时的责任使用的需求。