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arXiv · 2605.31381

LLM Judges Inconsistently Disagree Across Safety Criteria and Harm Categories

Krishnapriya Vishnubhotla·Sowmya Vajjala·Akriti Vij·Isar Nejadgholi
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Abstract

We evaluate the consistency of automated judges in conducting a multi-dimensional safety evaluation in a reference-free setup. Our results indicate that Large Language Models are unreliable judges in identifying safety issues related to machine-generated advice in regulated domains such as finance, although they are more reliable at identifying more overt forms of unsafe/harmful content such as violence. The degree of inconsistency in a model's judgments can vary significantly by the chosen safety criteria and can be impacted by the language of the content and its linguistic style as well. Finally, there is high disagreement among different judges for the same output, across domains, safety criteria, and languages. These findings provide new insights on the practice of using LLMs as evaluators and offer several recommendations for practitioners on how to use automated judges in practical scenarios.

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Krishnapriya Vishnubhotla, Sowmya Vajjala, Akriti Vij, Isar Nejadgholi. 2026-05-29. LLM Judges Inconsistently Disagree Across Safety Criteria and Harm Categories. https://arxiv.org/abs/2605.31381

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