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MINCE: Shrinking LLM Evaluation Datasets via Few-Model Monte Carlo Calibration
Evaluating LLMs across many model variants---quantized, fine-tuned, or deployment-specific---requires running large benchmarks repeatedly, a process that can take tens of hours per model on edge hardware such as NPUs. Existing subset selection methods reduce this cost but depend on large calibration
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