Commonsense reasoning — answering questions that require everyday knowledge about how the physical and social world works — is measured by benchmarks like CommonsenseQA, PIQA, and HellaSwag. Large language models have largely saturated early benchmarks (HellaSwag went from 95% to near-ceiling by 2023), forcing a shift to harder tests like ARC-Challenge and Winoground. The uncomfortable insight is that scale alone buys enormous commonsense performance, but adversarial probing still reveals brittle failures on spatial reasoning, temporal logic, and physical intuition that humans find trivial.
Broad multi-task language-understanding benchmark with 57 subjects spanning STEM, humanities, social sciences, and professional knowledge. Original 4-choice MCQ format; now saturated enough that top-frontier deltas should be read as a cluster rather than a strict ranking.
Leading models on MMLU.
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6 datasets tracked for this task.
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