Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs

View PDF HTML (experimental) Abstract:Hard-gated safety checkers often over-refuse and misalign with a vendor's model spec; prevailing taxonomies also neglect robustness and honesty, yielding safer-on-paper yet less useful systems. This work introduces Guardian-as-an-Advisor (GaaA), a soft-gating pipeline where a guardian predicts a binary risk label plus a concise explanation and prepends this advice to the original query for re-inference, keeping the base model operating under its original spec. To support training and evaluation, GuardSet is constructed, a 208k+ multi-domain dataset unifying harmful and harmless cases with targeted robustness and honesty slices. GuardAdvisor is trained via SFT followed by RL to enforce label-explanation consistency. GuardAdvisor attains competitive detection accuracy while enabling the advisory workflow; when used to augment inputs, responses improve over unaugmented prompts. A latency study shows advisor inference uses below 5% of base-model compute and adds only 2-10% end-to-end overhead under realistic harmful-input rates. Overall, GaaA steers models to comply with the model spec, maintaining safety while reducing over-refusal. Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL) Cite as: arXiv:2604.07655 [cs.LG] (or arXiv:2604.07655v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2604.07655 arXiv-issued DOI via DataCite (pending registration) Submission history From: Yue Huang [view email] [v1] Wed, 8 Apr 2026 23:47:29 UTC (1,669 KB)