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Evaluating Model Capabilities After Benchmark Saturation
Author1, Author2, Author3, Author4, Author5
benchmarkingmodel evaluationcapability assessmentAI safety
2610.20667
Builder Relevance
1h ago80%
Abstract
The paper investigates the utility of model transcripts from saturated benchmarks to assess model capabilities.
Reality Card
Core Claim
Saturated benchmark transcripts retain capability information beyond benchmark accuracy, with pairwise judging being the most effective method for extracting this information.
Method / Result
Pairwise judging reduces prediction error by 5.3 times compared to using only saturated benchmark scores.
Limitations
The predictive power of protocols does not generalize across distribution shifts, leading to potential underestimation of capabilities.
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