@inproceedings{rad-etal-2025-graid,
    title = "{GRAID}: Synthetic Data Generation with Geometric Constraints and Multi-Agentic Reflection for Harmful Content Detection",
    author = "Rad, Melissa Kazemi  and
      Purpura, Alberto  and
      Kumar, Himanshu  and
      Chen, Emily  and
      Sorower, Mohammad Shahed",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1528/",
    pages = "30047--30065",
    ISBN = "979-8-89176-332-6",
    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."
}