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Binarization Flattens the Score Space
Not provided
evaluationlarge language modelsgradingperformance assessment
2609.35797
Builder Relevance
1h ago70%
Abstract
The paper discusses the limitations of using binary pass/fail verdicts from large language model judges and proposes a method to retain more nuanced grading to better capture performance.
Reality Card
Core Claim
Retaining at least three grades in evaluation improves the assessment of model performance by addressing ambiguities introduced by binarization.
Method / Result
At n=1,024, the test showed at least 96.5% power at a 1.5× stress.
Limitations
The shared-factor reference approximation fit MATH and SciBench but not HealthBench, indicating limited empirical scope.
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