{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"boolq","formal_name":"BoolQ","introduction":"BoolQ pairs naturally occurring yes/no questions from search queries with a passage that decides them. The format is simple, but the inference it needs is consistently heavier than it looks.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://github.com/google-research-datasets/boolean-questions","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":"4fdf3eaa-b70b-52fe-a3e2-075e27bbffde","task_key":"default--validation--4fdf3eaa-b70b-52fe-a3e2-075e27bbffde","task_revision_id":"1","upstream_id":"","short_description":"is least squares the same as linear regression","config":"default","split":"validation","body":"{\"passage\":\"In statistics, ordinary least squares (OLS) is a type of linear least squares method for estimating the unknown parameters in a linear regression model. OLS chooses the parameters of a linear function of a set of explanatory variables by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values of the variable being predicted) in the given dataset and those predicted by the linear function.\",\"question\":\"is least squares the same as linear regression\"}","display_format":"text","language":"","answer_status":"published","assets":[],"source_url":"https://github.com/google-research-datasets/boolean-questions","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}