The recent surge in research focused on generating synthetic data from large language models (LLMs), especially for scenarios with limited data availability, marks a notable shift in Generative Artificial Intelligence (AI). Their ability to perform comparably to real-world data positions this approach as a compelling solution to low-resource challenges. This paper delves into advanced technologies that leverage these gigantic LLMs for the generation of task-specific training data. We outline methodologies, evaluation techniques, and practical applications, discuss the current limitations, and suggest potential pathways for future research.

翻译：近期聚焦于从大型语言模型（LLMs）生成合成数据的研究激增，尤其针对数据稀缺场景，这标志着生成式人工智能（AI）领域的显著转变。此类模型生成的数据在性能上可与真实世界数据相媲美，使其成为应对低资源挑战的有力解决方案。本文深入探讨了利用这些巨型LLMs生成任务特定训练数据的前沿技术。我们概述了相关方法、评估技术及实际应用，讨论了当前局限性，并提出了未来研究的潜在方向。