Rethinking Quality in Large Language Model Routing: Security-Conditioned Model-and-Budget Selection
We propose SCRouter, a router that learns when to prioritize security and how to allocate the token budget.
ProofPaper ↗
Key points
- Large language model (LLM) routers reduce serving cost by choosing which model to call and how many output tokens to grant it for each query.
- Existing routers rank model-budget actions using a single quality metric.
- For ordinary queries, a good answer is correct and useful (utility); for security-sensitive queries, a good answer refuses harmful requests or resists malicious instructions (security).
- We also build SCRouterBench, a benchmark of 38,869 utility and security queries spanning nine security dimensions, each answered under all 144 combinations of 12 models and 12 budgets, plus 2,753 out-of-domain queries.
Sources (1)
- [1]Rethinking Quality in Large Language Model Routing: Security-Conditioned Model-and-Budget SelectionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 03:36 PM
We propose SCRouter, a router that learns when to prioritize security and how to allocate the token budget.
Large language model (LLM) routers reduce serving cost by choosing which model to call and how many output tokens to grant it for each query.
Extractive summary: sentences quoted from the sources.
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