Three Ways Classical Test Theory Misleads for LLM Judges

Abstract:An LLM judge scores a bank of responses against a rubric, and the reliability comes back at $0.52$. What has been measured? Judge evaluation has begun borrowing reliability statistics from classical test theory, usually without stating the measurement design each statistic assumes, and we show that three widely portable ones mean something different for a judge than for a test because the judge setting rearranges the roles those designs rest on. First, an internal-consistency coefficient computed over rubric elements contains no scorer facet. Holding one judge's measured error rate fixed at $4.72\%$, KR-20 still ranges from $0.01$ to $0.68$ as the item bank is redesigned around it, and varying judge error moves the coefficient by a comparable amount, so item design and judge error are not separately identified and no single value can be read as a property of the judge. Second, the dependability index $\Phi(\lambda)$ is a ratio of variance components, and the classification probability with which it is sometimes identified differs from it by $0.25$-$0.43$ on our bank and by $0.17$-$0.30$ on simulated data where the underlying model holds exactly. Third, Livingston-Lewis accuracy is indexed to an examinee's own true score on the same instrument, so scoring it against external gold conflates judge unreliability with criterion invalidity. Reviewing the three closest judge-evaluation papers, we found no published instance of these errors, which makes the caution prospective. A coefficient that cannot be attributed to the judge nonetheless travels downstream into deployment decisions and disclosure documents. We therefore close with four reporting lines that keep the attribution attached to the number.

5 pages plus references and appendix (12 pages total), 4 figures. Code and data: this https URL

Machine Learning (cs.LG); Computation and Language (cs.CL); Methodology (stat.ME)

arXiv:2609.29709 [cs.LG]

(or arXiv:2609.29709v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.29709 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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