@inproceedings{triantafyllopoulos-etal-2026-knowing,
    title = "Knowing When Not to Answer: Lightweight {KB}-Aligned {OOD} Detection for Safe {RAG}",
    author = "Triantafyllopoulos, Ilias  and
      Qu, Renyi  and
      Giorgi, Salvatore  and
      Curtis, Brenda  and
      Ungar, Lyle  and
      Sedoc, Jo{\textbackslash}{\textasciitilde}ao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.740/",
    pages = "16266--16301",
    ISBN = "979-8-89176-390-6",
    abstract = "Retrieval-Augmented Generation (RAG) systems are increasingly deployed in high-stakes domains, where safety depends not only on \textit{how} a system answers, but also on \textit{whether} a query should be answered given a knowledge base (KB). Out-of-domain (OOD) queries can cause dense retrieval to surface weakly related context and lead the generator to produce fluent but unjustified responses. We study lightweight, KB-aligned OOD detection as an always-on gate for RAG systems. Our approach applies PCA to KB embeddings and scores queries in a compact subspace selected either by explained-variance retention (EVR) or by a separability-driven -test ranking. We evaluate geometric semantic-search rules and lightweight classifiers across 16 domains, including high-stakes COVID-19 and Substance Use KBs, and stress-test robustness using both LLM-generated attacks and an in-the-wild 4chan attack. We find that low-dimensional detectors achieve competitive OOD performance while being faster, cheaper, and more interpretable than prompted LLM-based judges. Finally, human and LLM-based evaluations show that OOD queries primarily degrade the \textit{relevance} of RAG outputs, highlighting the need for efficient external OOD detection to maintain safe, in-scope behavior."
}