{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"mmlu-pro","formal_name":"MMLU-Pro","introduction":"MMLU-Pro rebuilds MMLU with ten answer options instead of four and removes items that no longer separate models. Questions span fourteen academic and professional subjects.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro","indexing_mode":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"c7818f19-667b-53ed-a0e3-ddd89a405bbe","task_key":"default--test--10365","task_revision_id":"1","upstream_id":"10365","short_description":"Statement 1| RELUs are not monotonic, but sigmoids are monotonic. Statement 2|…","config":"default","split":"test","body":"{\"category\":\"computer science\",\"options\":\"['True, False', 'True, True', 'Both statements are partially true', 'Statement 1 cannot be determined, Statement 2 is false', 'Both statements cannot be determined', 'False, False', 'Statement 1 is false, Statement 2 cannot be determined', 'Statement 1 cannot be determined, Statement 2 is true', 'Statement 1 is true, Statement 2 cannot be determined', 'False, True']\",\"question\":\"Statement 1| RELUs are not monotonic, but sigmoids are monotonic. Statement 2| Neural networks trained with gradient descent with high probability converge to the global optimum.\",\"src\":\"ori_mmlu-machine_learning\"}","display_format":"text","language":"","answer_status":"published","assets":[],"source_url":"https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}