nvidia/Nemotron-Content-Safety-Reasoning-Dataset

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@inproceedings{sreedhar-etal-2025-safety,
    title = "Safety Through Reasoning: An Empirical Study of Reasoning Guardrail Models",
    author = "Sreedhar, Makesh Narsimhan  and
      Rebedea, Traian  and
      Parisien, Christopher",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-emnlp.1193/",
    pages = "21862--21880",
    ISBN = "979-8-89176-335-7",
    abstract = "Reasoning-based language models have demonstrated strong performance across various domains, with the most notable gains seen in mathematical and coding tasks. Recent research has shown that reasoning also offers significant benefits for LLM safety and guardrail applications. In this work, we conduct a comprehensive analysis of training reasoning-based guardrail models for content moderation, with an emphasis on generalization to custom safety policies at inference time. Our study focuses on two key dimensions: data efficiency and inference efficiency. On the data front, we find that reasoning-based models exhibit strong sample efficiency, achieving competitive performance with significantly fewer training examples than their non-reasoning counterparts. This unlocks the po

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dataset_licence({ "id": "nvidia/Nemotron-Content-Safety-Reasoning-Dataset" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0