Abstract
The increasing use of synthetic data generated by Large Language Models (LLMs) presents both opportunities and challenges in data-driven applications. While synthetic data provides a cost-effective, scalable alternative to real-world data to facilitate model training, its diversity and privacy risks remain underexplored. Focusing on text-based synthetic data, we propose a comprehensive set of metrics to quantitatively assess the diversity (i.e., linguistic expression, sentiment, and user perspective), and privacy (i.e., re-identification risk and stylistic outliers) of synthetic datasets generated by several state-of-the-art LLMs. Experiment results reveal significant limitations in LLMs' capabilities in generating diverse and privacy-preserving synthetic data. Guided by the evaluation results, a prompt-based approach is proposed to enhance the diversity of synthetic reviews while preserving reviewer privacy.
Abstract (translated)
大型语言模型（LLM）生成的合成数据在数据驱动应用程序中既带来了机会也带来了挑战。尽管合成数据为现实世界数据提供了一种成本效益高且可扩展的替代方案，以促进模型训练，但其多样性和隐私风险仍然有待深入研究。专注于基于文本的合成数据，我们提出了一系列全面的指标来定量评估由几种最先进的LLM生成的数据集在多样性（即语言表达、情感和用户视角）和隐私（即再识别风险和风格异常值）方面的表现。实验结果揭示了LLM在生成多样性和保护隐私的合成数据方面存在显著局限性。根据评估结果，我们提出了一种基于提示的方法来增强合成评论的多样性，同时保持评论者的隐私。
URL
https://arxiv.org/abs/2507.18055
