TruthfulQA is a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. Questions are crafted so that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts. The text in the dataset is in English. The associated BCP-47 code is en. Note: Both generation and multiplechoice configurations have the same questions. An example of generation looks as follows: An example of multiplechoice looks as follows: - type: A string denoting whether the…
SAVRN Model Hub · Datasets by Task
Multiple choice Datasets
2 datasets in the SAVRN Model Hub for multiple choice, from publishers including TruthfulQA, Eduard Hovy.
2 datasets.
RACE is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The dataset is collected from English examinations in China, which are designed for middle school and high school students. The dataset can be served as the training and test sets for machine comprehension. An example of 'train' looks as follows. An example of 'train' looks as follows. An example of 'train' looks as follows. The data fields are the same among all splits. - exampleid: a string feature. - article: a string feature. - answer: a string feature. - question: a string feature. - options: a list of string features. - exampleid: a string feature. - article: a string…