Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs
arXiv cs.CL / 4/10/2026
💬 OpinionIdeas & Deep AnalysisModels & Research
Key Points
- Hard-gated safety checkers can over-refuse and conflict with a vendor model’s specification, motivating a softer, spec-preserving safety approach for LLMs.
- The paper proposes “Guardian-as-an-Advisor (GaaA),” where a guardian predicts a risk label with a brief explanation and prepends that advice to the user query for re-inference while keeping the base model within its original spec.
- To train and evaluate this workflow, the authors introduce “GuardSet,” a 208k+ multi-domain dataset that includes dedicated robustness and honesty slices alongside harmful/harmless examples.
- Training uses supervised fine-tuning followed by reinforcement learning to enforce consistency between risk labels and explanations, yielding strong detection performance and better downstream responses when inputs are augmented.
- A latency study reports that advisor inference costs <5% of base-model compute and adds only 2–10% end-to-end overhead under realistic harmful-input rates, while reducing over-refusal.
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