{"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":"a28e6873-0fb6-5bc9-853d-06d78b7746ba","task_key":"default--test--10486","task_revision_id":"1","upstream_id":"10486","short_description":"Statement 1| For a continuous random variable x and its probability distribution…","config":"default","split":"test","body":"{\"category\":\"computer science\",\"options\":\"['Statement 1 is a well-known inequality in calculus, Statement 2 is a common method in data mining', 'Statement 1 is a theorem in real analysis, Statement 2 is a principle in information theory', 'Statement 1 is a fundamental principle of statistics, Statement 2 is a rule of thumb in decision analysis', 'False, True', 'False, False', 'True, True', 'Statement 1 is a fundamental principle of probability, Statement 2 is a common algorithm for machine learning', 'Statement 1 is a key concept in measure theory, Statement 2 is a method used in decision theory', 'Statement 1 is a property of all random variables, Statement 2 is a heuristic used in artificial intelligence', 'True, False']\",\"question\":\"Statement 1| For a continuous random variable x and its probability distribution function p(x), it holds that 0 ≤ p(x) ≤ 1 for all x. Statement 2| Decision tree is learned by minimizing information gain.\",\"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":[]}