Semantic Certainty Assessment in Vector Retrieval Systems: A Novel Framework for Embedding Quality Evaluation

TL;DR

This paper introduces a lightweight framework for assessing the quality of embeddings in vector retrieval systems at the query level, improving retrieval performance and enabling adaptive strategies.

Contribution

The novel framework combines quantization robustness and neighborhood density metrics to predict retrieval success with minimal computational overhead.

Findings

Achieved 9.4% average improvement in Recall@10 on four datasets.

Framework requires less than 5% of retrieval time for performance prediction.

Revealed systematic patterns in embedding quality across query types.

Abstract

Vector retrieval systems exhibit significant performance variance across queries due to heterogeneous embedding quality. We propose a lightweight framework for predicting retrieval performance at the query level by combining quantization robustness and neighborhood density metrics. Our approach is motivated by the observation that high-quality embeddings occupy geometrically stable regions in the embedding space and exhibit consistent neighborhood structures. We evaluate our method on 4 standard retrieval datasets, showing consistent improvements of 9.4$\pm$1.2\% in Recall@10 over competitive baselines. The framework requires minimal computational overhead (less than 5\% of retrieval time) and enables adaptive retrieval strategies. Our analysis reveals systematic patterns in embedding quality across different query types, providing insights for targeted training data augmentation.