  We propose a new test to measure a text model's multitask accuracy. The test
covers 57 tasks including elementary mathematics, US history, computer science,
law, and more. To attain high accuracy on this test, models must possess
extensive world knowledge and problem solving ability. We find that while most
recent models have near random-chance accuracy, the very largest GPT-3 model
improves over random chance by almost 20 percentage points on average. However,
on every one of the 57 tasks, the best models still need substantial
improvements before they can reach expert-level accuracy. Models also have
lopsided performance and frequently do not know when they are wrong. Worse,
they still have near-random accuracy on some socially important subjects such
as morality and law. By comprehensively evaluating the breadth and depth of a
model's academic and professional understanding, our test can be used to
analyze models across many tasks and to identify important shortcomings.
