  Cloze testing is a common method for measuring the behavior of large language
models on a number of benchmark tasks. Using the MMLU dataset, we show that the
base-rate probability (BRP) differences across answer tokens are significant
and affect task performance ie. guess A if uncertain. We find that
counterfactual prompting does sufficiently mitigate the BRP effect. The BRP
effect is found to have a similar effect to test taking strategies employed by
humans leading to the conflation of task performance and test-taking ability.
We propose the Nvr-X-MMLU task, a variation of MMLU, which helps to
disambiguate test-taking ability from task performance and reports the latter.
