{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"mmlu","formal_name":"MMLU","introduction":"MMLU asks four-way multiple-choice questions across 57 subjects, from elementary material to professional-level examinations. Since 2020 it has been the most widely cited single reference point for general capability.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://github.com/hendrycks/test","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":"fadd2364-94dd-5965-859f-0f251c49a6ae","task_key":"all--test--fadd2364-94dd-5965-859f-0f251c49a6ae","task_revision_id":"1","upstream_id":"","short_description":"Which of the following is/are true regarding an SVM?","config":"all","split":"test","body":"{\"choices\":\"['For two dimensional data points, the separating hyperplane learnt by a linear SVM will be a straight line.', 'In theory, a Gaussian kernel SVM cannot model any complex separating hyperplane.', 'For every kernel function used in a SVM, one can obtain an equivalent closed form basis expansion.', 'Overfitting in an SVM is not a function of number of support vectors.']\",\"question\":\"Which of the following is/are true regarding an SVM?\",\"subject\":\"machine_learning\"}","display_format":"text","language":"","answer_status":"published","assets":[],"source_url":"https://github.com/hendrycks/test","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}