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.

翻译：人工智能模型的成功依赖于大规模、多样化且高质量数据集的可用性，但由于数据稀缺、隐私问题和高昂成本，获取此类数据往往面临挑战。合成数据通过生成模拟真实世界模式的人工数据，已成为一个有前景的解决方案。本文概述了合成数据研究，讨论了其应用、挑战和未来方向。我们通过先前研究的实证证据展示了其有效性，并强调了确保其真实性、保真度和无偏性的重要性。我们呼吁负责任地使用合成数据，以构建更强大、更具包容性和更可信的语言模型。