Capturing LLM Capabilities via Evidence-Calibrated Query Clustering

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llm query-clustering evidence-calibrated capability-evaluation bradley-terry-model semantic-embeddings

Summary

This paper proposes Evidence-Calibrated Query Clustering (ECC), an algorithm that aligns semantic embeddings with latent LLM capability demands using posterior model comparisons and Bradley-Terry modeling, significantly improving capability ranking quality for LLM evaluation.

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.
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Paper page - Capturing LLM Capabilities via Evidence-Calibrated Query Clustering

Source: https://huggingface.co/papers/2605.17110

Abstract

Query clustering algorithm ECC improves LLM capability evaluation by aligning semantic embeddings with latent capability demands through posterior model comparisons and Bradley-Terry modeling.

Query clusteringorganizes queries into groups that reflect sharedlatent 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 priorsemantic embeddingsusing limitedposterior model comparisonsto bridge the gap between surface-level semantics and latent capability requirements. ECC characterizes each cluster through a capability profile parameterized by aBradley-Terry modeland usestrainable mixture weightsto accommodate queries with mixed capability demands, jointly learning a flexible,capability-aware clusteringstructure that supports query-specific inference of LLM capabilities. Extensive quantitative and qualitative evaluations demonstrate that ECC significantly improvesLLM capability rankingquality, 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 asquery routing.

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