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 pools or learned prediction layers. We introduce MINCE (Monte Carlo Informed N-sizing for Compact Evaluation), which uses Monte Carlo simulation over per-item logs from a small set of calibration models to find the minimum subset size that bounds accuracy drift and then fixes a randomly sampled subset at that size, with no prediction layer needed. MINCE reduces IFEVAL by 54\%, MMLU by 89\%, GS...
| Authors | Devleena Das, Rajeev Patwari, Vikram Kumar Bukka et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 June 2026 |
| Last updated at source | 30 September 2026 |
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