论文标题
规定的合成数据 - 学习的通用语言
Rule-adhering synthetic data -- the lingua franca of learning
论文作者
论文摘要
AI生成的合成数据允许提炼现有数据的一般模式，然后可以在原始语义中安全地将其作为颗粒级代表性，但新颖的数据样本共享。在这项工作中，我们探讨了将域专业知识纳入数据综合的方法，以表示统计属性以及预先存在的规则知识。可以为任何数量的新样本探测的由此产生的合成数据生成器，然后可以作为智力的共同来源，作为学习通用语言，由人类和机器都消耗。我们为公开数据集演示了该概念，并通过描述性分析以及下游ML模型评估其收益。
AI-generated synthetic data allows to distill the general patterns of existing data, that can then be shared safely as granular-level representative, yet novel data samples within the original semantics. In this work we explore approaches of incorporating domain expertise into the data synthesis, to have the statistical properties as well as pre-existing domain knowledge of rules be represented. The resulting synthetic data generator, that can be probed for any number of new samples, can then serve as a common source of intelligence, as a lingua franca of learning, consumable by humans and machines alike. We demonstrate the concept for a publicly available data set, and evaluate its benefits via descriptive analysis as well as a downstream ML model.
