@inproceedings{zhang-etal-2026-improving,
    title = "Improving {LLM} Domain Certification with Pretrained Guide Models",
    author = "Zhang, Jiaqian  and
      Qian, Zhaozhi  and
      AL-Tam, Faroq  and
      Iacobacci, Ignacio  and
      AL-Qurishi, Muhammad  and
      Souissi, Riad",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.69/",
    pages = "1494--1510",
    ISBN = "979-8-89176-380-7",
    abstract = "Large language models (LLMs) often generate off-domain or harmful responses when deployed in specialized, high-stakes domains, motivating the need for rigorous LLM domain certification. While the VALID algorithm (Emde et al., 2025) achieves formal domain certificate guarantee using a guide model $G$ trained from scratch on in-domain data, it suffers from poor generalization due to limited training. In this work, we propose PRISM, a novel approach that overcomes this key limitation by leveraging pretrained language models as guide models, enhanced via contrastive fine-tuning to sharply distinguish acceptable from refused content. We explore and experiment variants of PRISM with different loss functions to ensure that the model exploits the rich world knowledge of pretrained models while aligned to the target domain. We show that two variants of PRISM, PRISM-BC and PRISM-GA, achieve superior OOD rejection and tighter certification bounds across eight diverse data regimes and perturbations, establishing a more reliable approach to domain-adherent LLM deployment."
}