Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation

• URL: http://arxiv.org/abs/2607.28801v1
• Date: Thu, 30 Jul 2026 19:51:55 GMT
• Title: Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation
• Abstract summary: We introduce a dataset-centric meta-evaluation framework that audits benchmark datasets at the sample level along five latent dimensions.<n>We show how these annotations enable criterion-driven orchestration of composite benchmark subsets across datasets.
• Score: 0.42970700836450487
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Benchmark datasets are central to evaluating Large Language Models (LLMs), yet they are typically conceived as monolithic tasks, obscuring substantial variation in the demands of individual samples. We introduce a dataset-centric meta-evaluation framework that audits benchmark datasets at the sample level along five latent dimensions: 1. Cognitive and Knowledge Demands, 2. Language and Content Quality, 3. Task Properties, 4. Context, and 5. Ethics, Safety, and Fairness. Applying this framework, we annotate five influential benchmarks -- MMLU, ARC, WinoGrande, HellaSwag, and TruthfulQA -- revealing pronounced internal heterogeneity that is not captured by aggregate accuracy scores. We show how these annotations enable criterion-driven orchestration of composite benchmark subsets across datasets, supporting targeted evaluation of model capabilities such as Reasoning Depth or Ethical Sensitivity. This approach reframes benchmark evaluation as dataset introspection, providing a principled methodology for analyzing and re-composing existing benchmarks to better reflect diverse evaluation needs.

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