A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models

arXiv:2404.14445v1 Announce Type: new
Abstract: The rapid advancements in generative AI and large language models (LLMs) have opened up new avenues for producing synthetic data, particularly in the realm of structured tabular formats, such as product reviews. Despite the potential benefits, concerns regarding privacy leakage have surfaced, especially when personal information is utilized in the training datasets. In addition, there is an absence of a comprehensive evaluation framework capable of quantitatively measuring the quality of the generated synthetic data and …

abstract arxiv benefits concerns cs.ai cs.cl cs.lg data evaluation framework generated generative language language models large language large language models llms privacy product product reviews realm reviews synthetic synthetic data tabular type