Evaluating Model Capabilities After Benchmark Saturation
Rapid advances in AI capabilities are causing benchmarks to saturate. Once several models approach perfect scores, a benchmark loses its power to differentiate between them, and with it its utility for informing AI safety cases and frontier policies. Yet benchmark items produce far more information than the scores we t...
Morio Hamada, Jehyeok Yeon, Matteo Migliarini et al.
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