VDAR-Router: Adaptive LLM Routing via Verbalized Query Difficulty Analysis
Researchers introduce VDAR-Router, a training-free, difficulty-aware framework for routing queries to large language models (LLMs). The system generates explicit difficulty analyses for each query and retrieves historical examples with similar difficulty profiles to estimate which model is most suitable, optimizing for both performance and cost. Experiments on three datasets show that VDAR-Router achieves better cost-performance trade-offs than existing routing methods.
Why it matters: This work presents a novel, training-free approach to LLM routing that explicitly incorporates query difficulty, offering a practical way to reduce deployment costs without sacrificing performance.
Full story at: arXiv Computation and Language ↗