Abstract
We address the problem of data scarcity in harmful text classification for guardrailing applications and introduce GRAID (Geometric and Reflective AI-Driven Data Augmentation), a novel pipeline that leverages Large Language Models (LLMs) for dataset augmentation. GRAID consists of two stages: (i) generation of geometrically controlled examples using a constrained LLM, and (ii) augmentation through a multi-agentic reflective process that promotes stylistic diversity and uncovers edge cases. This combination enables both reliable coverage of the input space and nuanced exploration of harmful content. Using two benchmark data sets, we demonstrate that augmenting a harmful text classification dataset with GRAID leads to significant improvements in downstream guardrail model performance.
Abstract (translated)
我们解决了护拦应用中危害性文本分类数据不足的问题，并引入了GRAID（几何和反思型AI驱动的数据增强），这是一种利用大型语言模型（LLMs）进行数据集扩增的新型流程。GRAID包括两个阶段：(i) 使用受约束的LLM生成几何控制示例，以及(ii) 通过一个多代理反思过程进行扩充，该过程促进了风格多样性并揭示了边缘案例。这种组合使得输入空间的可靠覆盖和有害内容的细腻探索成为可能。使用两个基准数据集，我们证明将GRAID用于扩增危害性文本分类数据集可以显著提高下游护拦模型的表现。
URL
https://arxiv.org/abs/2508.17057
