Query-efficient model evaluation using cached responses
Evaluating a new model on an existing benchmark is often necessary to understand its behavior before deployment. For modern evaluation frameworks, generating and evaluating a response for all queries can be prohibitively expensive. In practice, responses from previously-evaluated models are often cached -- creating a potential opportunity to use this additional information to decrease the number of queries required to accurately evaluate a new model. In this paper, we introduce an approach for predicting benchmark performance that leverages cached model responses based on the Data Kernel Perspective Space (DKPS), a method for quantifying the relationship between models in the black-box se...
| Authors | Hayden Helm, Ben Johnson, Carey Priebe |
|---|---|
| Primary category | cs.LG |
| Published | 8 May 2026 |
| Last updated at source | 4 June 2026 |
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