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The authors propose Synthetic Query Probing, a simple yet effective approach to compare different embedding models by comparing similarity match scores across models instead of raw embedding spaces. The paper illustrates non-linear relationships between models like Ada and Titan, offering practical guidance for swapping embedding models and setting retrieval thresholds.
Introduces Synthetic Query Probing (SQP), a reference-free method for learning mappings between similarity score distributions across embedding models, enabling threshold portability in RAG systems. Experiments show systematic score distortions and isotonic regression as the best calibration approach.