RubricEval: A Rubric-Level Meta-Evaluation Benchmark for LLM Judges in Instruction Following

• URL: http://arxiv.org/abs/2603.25133v1
• Date: Thu, 26 Mar 2026 07:55:32 GMT
• Title: RubricEval: A Rubric-Level Meta-Evaluation Benchmark for LLM Judges in Instruction Following
• Abstract summary: rubric-level evaluation has become a prevailing paradigm for evaluating instruction following in large language models (LLMs)<n>Our benchmark features: (1) the first rubric-level meta-evaluation benchmark for instruction following, (2) diverse instructions and responses spanning multiple instances and model sources, and (3) a substantial set of 3,486 quality-controlled subsets that better differentiates judge performance.<n>Considering evaluation paradigm, checklist-level evaluation outperforms rubric-level, explicit reasoning improves accuracy, and both together reduce inter-judge variance.
• Score: 46.45323577110897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rubric-based evaluation has become a prevailing paradigm for evaluating instruction following in large language models (LLMs). Despite its widespread use, the reliability of these rubric-level evaluations remains unclear, calling for meta-evaluation. However, prior meta-evaluation efforts largely focus on the response level, failing to assess the fine-grained judgment accuracy that rubric-based evaluation relies on. To bridge this gap, we introduce RubricEval. Our benchmark features: (1) the first rubric-level meta-evaluation benchmark for instruction following, (2) diverse instructions and responses spanning multiple categories and model sources, and (3) a substantial set of 3,486 quality-controlled instances, along with Easy/Hard subsets that better differentiates judge performance. Our experiments reveal that rubric-level judging remains far from solved: even GPT-4o, a widely adopted judge in instruction-following benchmarks, achieves only 55.97% on Hard subset. Considering evaluation paradigm, rubric-level evaluation outperforms checklist-level, explicit reasoning improves accuracy, and both together reduce inter-judge variance. Through our established rubric taxonomy, we further identify common failure modes and offer actionable insights for reliable instruction-following evaluation.

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