All Verdicts are Not Equal: Rethinking LLM Judge Reliability
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Computer Science > Computation and Language
arXiv:2610.12083 (cs)
[Submitted on 8 Oct 2026]
Title:All Verdicts are Not Equal: Rethinking LLM Judge Reliability
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Abstract:LLM-as-a-Judge is the standard paradigm for NLP evaluation, yet its systemic reliability remains poorly understood despite being widely treated as a deterministic ground truth. We present a comprehensive reliability audit, stresstesting six frontier models across four benchmarks, five prompt formats, two presentation orders, three sampling temperatures, and ten repetitions per condition. Our empirical analysis reveals severe vulnerabilities: verdicts change across identical replications at temperature zero, position-order swaps flip the majority of verdicts on challenging tasks, and the most deterministic judge achieves perfect consistency by trivially repeating incorrect verdicts, agreeing with ground truth only 51% of the time. To formalize these multi-faceted failure modes, we introduce the trustworthy verdict rate (T ), a unified metric capturing the joint probability that an evaluation is reproducible, order-invariant, and accurate. UsingT , we derive a theoretical upper bound on accuracy imposed by position bias and show that reliability is item-specific rather than modellevel. Finally, we demonstrate that shifting from pairwise win-rate to holistic rubric scoring improves trustworthiness more than any single-format prompting intervention, offering an actionable framework for robust NLP evaluation.
| Comments: | Accepted at AACL IJCNLP (Main) 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.12083 [cs.CL] |
| (or arXiv:2610.12083v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12083
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled All Verdicts are Not Equal: Rethinking LLM Judge Reliability, by Vineet Kumar and 2 other authors
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