Capturing LLM Capabilities via Evidence-Calibrated Query Clustering
For LLM evaluators and practitioners, ECC provides a method to automatically discover capability-aware query clusters, enabling more accurate and efficient model assessment and routing.
The paper introduces ECC, an algorithm that calibrates semantic embeddings with limited model comparisons to cluster queries by latent capability demands, improving LLM capability ranking by 17.64-18.02 percentage points over baselines and proving effective for query routing.
Query clustering organizes queries into groups that reflect shared latent capability demands, enabling capability-aware LLM evaluation. Existing clustering methods, which primarily rely on semantic taxonomies or embeddings, often fail to capture such latent capability requirements due to a misalignment between surface-level semantics and actual model performance. We propose ECC, an algorithm that calibrates prior semantic embeddings using limited posterior model comparisons to bridge the gap between surface-level semantics and latent capability requirements. ECC characterizes each cluster through a capability profile parameterized by a Bradley-Terry model and uses trainable mixture weights to accommodate queries with mixed capability demands, jointly learning a flexible, capability-aware clustering structure that supports query-specific inference of LLM capabilities. Extensive quantitative and qualitative evaluations demonstrate that ECC significantly improves LLM capability ranking quality, outperforming human-labeled and embedding-based baselines by an average of 17.64 and 18.02 percentage points, respectively, and proves effective in downstream tasks such as query routing.