Query-Efficient Model Evaluation via Cached Responses
A novel technique outlined in arXiv:2605.07096 utilizes previously stored model responses to minimize the number of queries required for assessing new models against benchmarks. This strategy, rooted in the Data Kernel Perspective Space (DKPS), measures the connections between models in a black-box environment. Theoretically, methods based on DKPS demonstrate query efficiency under specific circumstances. In practice, they attain comparable mean absolute error to baseline models while significantly lowering the query expenditure. This innovation tackles the substantial expenses associated with generating and evaluating responses for every query within contemporary evaluation systems.
Key facts
- arXiv:2605.07096 introduces a method for predicting benchmark performance using cached model responses.
- The method is based on the Data Kernel Perspective Space (DKPS).
- DKPS quantifies relationships between models in a black-box setting.
- The approach is theoretically query-efficient under certain conditions.
- Empirically, DKPS-based methods achieve the same mean absolute error as baselines with fewer queries.
- The technique addresses the high cost of evaluating new models on existing benchmarks.
Entities
Institutions
- arXiv