Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation
Introduces a dataset-centric meta-evaluation framework that audits benchmark datasets at the sample level rather than treating them as monolithic tasks Audits benchmarks along five latent dimensions: Cognitive and Knowledge Demands, Language and Content Quality, Task Properties, Context, and Ethics/Safety/Fairness Applied to five influential benchmarks (MMLU, ARC, WinoGrande, HellaSwag, TruthfulQA), revealing pronounced internal heterogeneity invisible in aggregate accuracy scores Enables criter
Analysis
TL;DR
- Introduces a dataset-centric meta-evaluation framework that audits benchmark datasets at the sample level rather than treating them as monolithic tasks
- Audits benchmarks along five latent dimensions: Cognitive and Knowledge Demands, Language and Content Quality, Task Properties, Context, and Ethics/Safety/Fairness
- Applied to five influential benchmarks (MMLU, ARC, WinoGrande, HellaSwag, TruthfulQA), revealing pronounced internal heterogeneity invisible in aggregate accuracy scores
- Enables criterion-driven orchestration of composite benchmark subsets across datasets for targeted evaluation of specific model capabilities
- Reframes benchmark evaluation as dataset introspection, offering a principled methodology for analyzing and re-composing existing benchmarks
Why It Matters
This work addresses a critical gap in LLM evaluation: aggregate benchmark scores mask substantial variation in sample-level demands, making it difficult to assess specific capabilities like reasoning depth or ethical sensitivity. For AI practitioners and researchers, this framework provides a practical methodology to move beyond one-size-fits-all benchmarking and instead construct targeted, purpose-driven evaluations from existing datasets.
Technical Details
- Five-dimensional audit framework: Each sample in a benchmark is annotated along five latent dimensions — Cognitive and Knowledge Demands, Language and Content Quality, Task Properties, Context, and Ethics, Safety, and Fairness
- Benchmarks evaluated: MMLU, ARC, WinoGrande, HellaSwag, and TruthfulQA were audited using this framework, with results showing significant internal heterogeneity not captured by standard aggregate metrics
- Criterion-driven orchestration: The sample-level annotations enable the construction of composite benchmark subsets across datasets, tailored to evaluate specific capabilities such as Reasoning Depth or Ethical Sensitivity
- Meta-evaluation approach: Rather than evaluating models directly, the framework performs introspection on benchmark datasets themselves, treating them as composables resources rather than fixed evaluation artifacts
Industry Insight
- Benchmark designers and evaluators should consider moving toward sample-level annotation standards to enable more nuanced and targeted model assessment
- The orchestration approach could become a foundational practice for creating domain-specific or capability-specific evaluation suites without requiring entirely new dataset collection
- Organizations deploying LLMs in high-stakes contexts (healthcare, legal, education) can leverage this framework to construct evaluation subsets that closely mirror their specific operational requirements and risk profiles
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