Your Benchmark Is an Empirical Measure Over Difficulty
Your Benchmark Is an Empirical Measure Over Difficulty
Research formalizes benchmark evaluation through difficulty measures to identify measurement mismatches and coverage gaps, proposing diagnostic metrics and rewrite algorithms to improve benchmark coverage; analyzing 25 popular benchmarks across 7 fields reveals that most implicitly overestimate specific difficulty areas or fail to cover most of the difficulty axis, limiting model improvement monitoring capabilities; introduces BenchFill algorithm to effectively reduce local coverage gaps.
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, 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.
View arXiv page View PDF Project page GitHub 27 Add to collection
Get this paper in your agent:
hf papers read 2610.29927
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash
Models citing this paper 0
No model linking this paper
Cite arxiv.org/abs/2610.29927 in a model README.md to link it from this page.
Datasets citing this paper 1
Yifan99/BenchFill
Viewer • Updated 8 days ago • 235 • 238 • 4
Spaces citing this paper 0
No Space linking this paper
Cite arxiv.org/abs/2610.29927 in a Space README.md to link it from this page.
Collections including this paper 0
No Collection including this paper
Add this paper to a collection to link it from this page.
Source: Hugging Face Daily Papers · huggingface.co