RADAR: Rubric-Aware Dependency and Redundancy Analysis for LLM-as-Judge Evaluation

Abstract:Rubric-based LLM-as-judge pipelines often assume that evaluation criteria provide independent signals. In practice, however, criteria can be behaviorally coupled: improving one criterion may systematically change scores on another, distorting aggregate scores used in model-release or product-update decisions. We introduce RADAR, a lightweight preflight diagnostic framework for estimating such coupling before large-scale evaluation. Given a rubric, RADAR generates targeted synthetic probes, scores each probe on all criteria, and produces a directional coupling matrix that shows which criteria co-score and how. We validate RADAR on three industry-relevant evaluation settings: NVIDIA HelpSteer2, SumPubMed, and the Yale-Salesforce SummEval benchmark. Using only a small number of probes per criterion, RADAR recovers human inter-criterion correlation structure (Pearson r > 0.84) and provides practitioners with concrete audit signals about redundancy, hierarchy, and aggregation sensitivity before committing to large-scale judging.

Computation and Language (cs.CL)

arXiv:2608.01810 [cs.CL]

(or arXiv:2608.01810v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.01810 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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