Your Benchmark Is an Empirical Measure Over Difficulty

Hugging Face Daily Papers Papers
benchmarking evaluation-metrics difficulty-measurement ai-evaluation diagnostic-metrics item-rewriting

Summary

This paper formalizes benchmark evaluation as an empirical measure over question difficulty, identifying failure modes like measure mismatch and coverage gaps, and proposes diagnostic metrics plus BenchFill, a difficulty-targeted item rewriting algorithm, with analysis across 25 popular benchmarks in 7 domains.

A benchmark is not merely a collection of test questions; it naturally induces an empirical measure over difficulty. In this work, we develop this measure-based view of benchmark evaluation. This perspective allows us to identify and formalize two central failure modes: measure mismatch, where the benchmark difficulty distribution differs from the intended difficulty distribution, and coverage gaps, where uncovered regions along the difficulty axis prevent benchmarks from detecting meaningful localized training progress. We propose two diagnostic metrics that capture these two failure modes and provide rigorous theoretical guarantees. Empirically, we analyze 25 popular benchmarks spanning 7 domains using carefully estimated item difficulties. Our results show that many benchmarks implicitly overweight certain difficulty regions, so their leaderboard conclusions should be interpreted with caution; others leave substantial portions of the difficulty axis uncovered, limiting their ability to monitor the model improvement process. Finally, we turn diagnosis into action by introducing BenchFill, a simple difficulty-targeted item rewriting algorithm that effectively reduces local coverage gaps.
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Source: https://huggingface.co/papers/2610.29927

Abstract

This work formalizes benchmark evaluation through difficulty measures, identifies failure modes of measure mismatch and coverage gaps, and proposes diagnostic metrics and a rewriting algorithm to improve benchmark coverage.

A benchmark is not merely a collection of test questions; it naturally induces an empirical measure over difficulty. In this work, we develop this measure-based view of benchmark evaluation. This perspective allows us to identify and formalize two central failure modes:measure mismatch, where the benchmark difficulty distribution differs from the intended difficulty distribution, andcoverage gaps, where uncovered regions along the difficulty axis prevent benchmarks from detecting meaningful localized training progress. We propose twodiagnostic metricsthat capture these two failure modes and provide rigorous theoretical guarantees. Empirically, we analyze 25 popular benchmarks spanning 7 domains using carefully estimated item difficulties. Our results show that many benchmarks implicitly overweight certain difficulty regions, so their leaderboard conclusions should be interpreted with caution; others leave substantial portions of the difficulty axis uncovered, limiting their ability to monitor the model improvement process. Finally, we turn diagnosis into action by introducingBenchFill, a simpledifficulty-targeted item rewritingalgorithm that effectively reduces localcoverage gaps.

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