Designed to be more challenging than SQuAD-like datasets, they feature questions that require context of more than one document to answer, testing QA systems' abilities to infer the answer in the presence of multiple pieces of evidence and to efficiently find the evidence in a large pool of candidate documents.
since these datasets are still relatively new, most of the existing research focuses on the few-document setting where a relatively small set of context documents is given, which is guaranteed to contain the "gold" context documents, all those from which the answer comes (De Cao et al., 2019;Zhong et al., 2019).