SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety
  Guardrails in Large Language Models
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.
67b55b2cc92c4aa82c13562d
Seanie Lee
claimed_verified
2025-02-19T09:00:53.341Z
64ad5f59b7e4b2c1ce47eb43
/avatars/1f13ebe21a90d8c99920aa2c8cd9ac45.svg
Seanie Lee
user
Seanie-lee
67b55b2cc92c4aa82c13562e
Dong Bok Lee
67b55b2cc92c4aa82c13562f
Dominik Wagner
claimed_verified
2025-02-19T11:12:27.148Z
6311ba6f05cc08a1408d910a
https://cdn-avatars.huggingface.co/v1/production/uploads/1662997515866-6311ba6f05cc08a1408d910a.png
Dominik Wagner
user
dwgnr
67b55b2cc92c4aa82c135630
Minki Kang
67b55b2cc92c4aa82c135631
Haebin Seong
admin_assigned
2025-02-19T09:51:37.783Z
63a9379e2e05ca32e352d93b
/avatars/6cda37befc873a92ed6d5dcba507954a.svg
Haebin Seong
user
hbseong
67b55b2cc92c4aa82c135632
Tobias Bocklet
67b55b2cc92c4aa82c135633
Juho Lee
67b55b2cc92c4aa82c135634
Sung Ju Hwang
SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety
  Guardrails in Large Language Models
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.
SafeRoute improves safety and efficiency in deploying large language models by selectively routing hard examples to a large safety guard model.
large language models (LLMs) safety guard models distilled models binary router adaptive model selection