LLM Judges Inconsistently Disagree Across Safety Criteria and Harm Categories
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Aggregate vs. Personalized Judges in Business Idea Evaluation: Evidence from Expert Disagreement
Evaluating LLM-generated business ideas is often harder to scale than generating them. Unlike standard NLP benchmarks, business idea evaluation relies on multi-dimensional criteria such as feasibility, novelty, different…
From Rubrics to Reliable Scores: Evidence-Grounded Text Evaluation with LLM Judges
Rubric-based text evaluation increasingly relies on large language models (LLMs) as scalable judges, yet frozen black-box models can interpret the same criteria inconsistently, produce score attributions that are difficu…
Text GenerationReliable to Expressive: A Curriculum for Rubric-Following Safety Judges
Safety judges are increasingly deployed to evaluate model outputs against evolving criteria, yet recent meta-evaluation work shows they remain brittle under prompt and rubric variation, with false negative-rate swings of…
Quantifying the Statistical Effect of Rubric Modifications on Human-Autorater Agreement
Autoraters, also referred to as LLM-as-judges, are increasingly used for evaluation and automated content moderation. However, there is limited statistical analysis of how modifications in a rubric presented to both huma…
Who judges the judges? Governance from metrics: a runtime framework for continuous LLM compliance monitoring
Current approaches to AI compliance treat conformity as a binary, audit-time verdict rather than a continuous, measurable property of production systems. We argue that this compliance fiction is structurally ill-suited t…