SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety
  Guardrails in Large Language Models
Authors: name: Seanie Lee, name: Dong Bok Lee, name: Dominik Wagner, name: Minki Kang, name: Haebin Seong, name: Tobias Bocklet, name: Juho Lee, name: Sung Ju Hwang

Deploying large language models (LLMs) in real-world applications requires
robust safety guard models to detect and block harmful user prompts. While
large safety guard models achieve strong performance, their computational cost
is substantial. To mitigate this, smaller distilled models are used, but they
often underperform on "hard" examples where the larger model provides accurate
predictions. We observe that many inputs can be reliably handled by the smaller
model, while only a small fraction require the larger model's capacity.
Motivated by this, we propose SafeRoute, a binary router that distinguishes
hard examples from easy ones. Our method selectively applies the larger safety
guard model to the data that the router considers hard, improving efficiency
while maintaining accuracy compared to solely using the larger safety guard
model. Experimental results on multiple benchmark datasets demonstrate that our
adaptive model selection significantly enhances the trade-off between
computational cost and safety performance, outperforming relevant baselines.