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