Compare Embedding Models Through Similarity Spaces
Replacing embeddings? See how to recalibrate similarity thresholds instead of guessing.
30-Second TL;DR
What Changed
Compares embedding models through similarity scores for synthetic question–content pairs.
Why It Matters
Embedding model migrations often break manually tuned similarity thresholds. This approach could make retrieval evaluations more comparable across models and reduce trial-and-error during RAG system upgrades.
What To Do Next
Create a shared evaluation set of synthetic questions and chunks, then plot score distributions for your current and replacement embedding models before changing retrieval thresholds.
Key Points
- •Compares embedding models through similarity scores for synthetic question–content pairs.
- •Titan models with different dimensionalities show related similarity-score behavior.
- •Titan and Ada scores have different ranges and a nonlinear relationship.
- •The method can help calibrate retrieval thresholds when replacing embedding models.
- •The work is described in the paper “Similarity Spaces across Embedding Models with Synthetic Query Probing.”
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