Devleena Das

MINCE: Shrinking LLM Evaluation Datasets via Few-Model Monte Carlo Calibration

This work introduces 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...