A new research paper introduces Synthetic Query Probing, a method to analyze and map similarity score spaces across different embedding models. This technique addresses the challenge that scores are not directly comparable between models due to varying geometric properties. By generating queries from documents, the method allows for large-scale, reference-free analysis of cross-model similarity. Experiments on the SciFact dataset and a proprietary corpus demonstrate that while models generally agree on rankings, their absolute scores can be distorted. The research shows that learned mappings, particularly isotonic regression, can partially align these spaces and improve the portability of similarity thresholds. AI
IMPACT Improves RAG systems by enabling better comparison and portability of similarity scores across different embedding models.
RANK_REASON Research paper published on arXiv detailing a new method for analyzing embedding model comparability.
Read on Hugging Face Daily Papers →
- Hugging Face
- Isotonic regression
- retrieval-augmented generation
- SciFact
- Synthetic Query Probing
- arXiv
- Peter van der Putten
- quantile mappings
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