LLM Judges Inconsistently Disagree Across Safety Criteria and Harm Categories
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Computer Science > Computation and Language
Title:LLM Judges Inconsistently Disagree Across Safety Criteria and Harm Categories
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.
| Comments: | 8 pages plus appendices, under review |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.31381 [cs.CL] |
| (or arXiv:2605.31381v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.31381
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Krishnapriya Vishnubhotla [view email][v1] Fri, 29 May 2026 14:50:08 UTC (1,534 KB)
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