AI benchmarks also measure a difficulty distribution
In brief
- AI benchmarks implicitly measure a distribution of task difficulty.
- Two common failures: measure mismatch and coverage gaps in difficulty levels.
- BenchFill, an algorithm, fills gaps by rewriting targeted items.
A study introduces a new way to evaluate AI benchmarks by treating them as empirical measures of task difficulty. According to the authors, this approach highlights two key issues: a mismatch between measured and intended difficulty, and gaps in covering difficulty levels.
The researchers analyze 25 popular benchmarks across 7 domains and find that some overrepresent specific difficulty ranges, while others miss entire difficulty bands. They also propose BenchFill, an algorithm to address these gaps by rewriting items targeted at missing difficulty levels.
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