BenchMIRT: What are LLM benchmarks actually measuring?

BenchMIRT is an Ai2 method for auditing LLM benchmarks at the individual-question level, using multidimensional Item Response Theory to identify the underlying capabilities that influence benchmark performance rather than relying solely on aggregate scores. Trained on results from 100 LLMs across 16 reasoning and safety benchmarks containing more than 34,000 questions, it independently identified safety and general reasoning as stable dominant dimensions. Its analysis found that some benchmarks measure more than their stated purpose, such as BBQ and WMDP aligning more strongly with reasoning, while different HarmBench question types reflect different capabilities. BenchMIRT can also identify the most informative questions, with 10% of items often preserving relative assessments of model ability, and predict held-out model responses with 79% accuracy versus 70% for a benchmark-average baseline. The approach could support smaller, clearer, and more targeted evaluations, although its findings depend on the benchmarks and pre-March-2025 models analyzed, and its question-level insights could potentially be misused to weaken safety evaluations.

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