A well-tuned kNN router matches or exceeds state-of-the-art learned routers on new standardized benchmarks spanning instruction, QA, reasoning, and the first multi-modal visual routing dataset, due to locality of model performance in embedding space.
Open llm leaderboard v2
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
ECC calibrates semantic embeddings with posterior model comparisons and Bradley-Terry capability profiles to create flexible, mixed-membership query clusters that improve LLM capability ranking.
citing papers explorer
-
Rethinking Predictive Modeling for LLM Routing: When Simple kNN Beats Complex Learned Routers
A well-tuned kNN router matches or exceeds state-of-the-art learned routers on new standardized benchmarks spanning instruction, QA, reasoning, and the first multi-modal visual routing dataset, due to locality of model performance in embedding space.
-
Capturing LLM Capabilities via Evidence-Calibrated Query Clustering
ECC calibrates semantic embeddings with posterior model comparisons and Bradley-Terry capability profiles to create flexible, mixed-membership query clusters that improve LLM capability ranking.