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arXiv Machine Learning

A Closed-Form Estimator and Diagnostic Battery for Anchor-Judge Error Correlation, Under a Single-Common-Factor Model

The paper presents a closed‑form estimator for the contamination correlation between anchors and judges under a single‑common‑factor model, requiring at least two judges and two anchors. It introduces a diagnostic battery—including judge‑covariance dispersion, over‑identification tests, a family‑block test, bootstrap confidence intervals, and a weak‑identification screen—to validate the estimator and detect violations. The authors also discuss identification limits for ordinal data and report that real panels have not yet passed the model‑adequacy pre‑test, while simulation studies confirm the estimator’s performance.

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arXiv Machine Learning
6d ago

Three Ways Classical Test Theory Misleads for LLM Judges

The article examines how classical test theory reliability statistics misrepresent the performance of large language model (LLM) judges. It shows that internal‑consistency coefficients, the dependability index, and Livingston‑Lewis accuracy each conflate judge error with item design or criterion validity, making it impossible to attribute a single reliability value to the judge alone. The authors argue that such misattribution can influence deployment decisions and documentation.

By Louis Yiven Zhu
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arXiv AI
Sep 1

Benchmark Contamination: A Taxonomy Organized by Defeated Mitigation

The paper introduces a new taxonomy for benchmark contamination that categorizes leakage by the mitigation it defeats—direct, derivative, temporal, distributional, and acquired—covering both training‑time and evaluation‑time scenarios. It proposes a four‑field disclosure protocol to record contamination status alongside benchmark scores, and provides a JSON schema, validator, and examples. An empirical study of 41 documents using a pre‑registered instrument shows limited reporting of contamination types and variable reliability, highlighting gaps in current disclosure practices.

By Johanna Angulo, V\'ictor Yeste, Hector Espinos-Morato
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arXiv AI
Aug 24

JuryProbe: An Empirical Consensus-Risk Diagnostic for Routing Reference-Free Factuality Judge Panels to Grounded Verification

JuryProbe is an empirical diagnostic tool designed to assess consensus risk in panels of reference‑free large language model judges used for factuality verification. It estimates risk by measuring false‑negative correlations and false‑consensus lift from a labeled calibration probe, and routes high‑risk majority decisions to judges with trusted references. The approach was validated on FEVER corruptions, showing that flagged decisions can be grounded without additional reference acquisition in most cases, while reducing false accepts by about 0.4% and avoiding 28% of reference acquisitions.

By Tianxin Zhou, Ruixi Lin
llmsbenchmarks
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arXiv Machine Learning
Jul 1

Calibration, Not Compilation: Detecting and Repairing Misspecified Probabilistic Programs Written by Language Models

arXiv:2606. 31630v1 Announce Type: new Abstract: Language models increasingly write probabilistic programs (in NumPyro, Stan, or Pyro), but a program that compiles, runs, and passes every unit test can still be \emph{statistically} wrong -- a Gaussian likelihood for heavy-tailed data, a Poisson for over-dispersed counts, an invalid prior support, or a pathological parameterization.

By Jian Xu, Delu Zeng, John Paisley, Qibin Zhao
llmsbenchmarks
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