OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic (with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In particular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge, and rich text comprehension.

Social Interaction QA (SIQA) is a question-answering benchmark for testing social commonsense intelligence. Contrary to many prior benchmarks that focus on physical or taxonomic knowledge, Social IQa focuses on reasoning about people’s actions and their social implications. For example, given an action like "Jesse saw a concert" and a question like "Why did Jesse do this?", humans can easily infer that Jesse wanted "to see their favorite performer" or "to enjoy the music", and not "to see what's happening inside" or "to see if it works". The actions in Social IQa span a wide variety of social situations, and answer candidates contain both human-curated answers and adversarially-filtered machine-generated candidates. Social IQa contains over 37,000 QA pairs for evaluating models’ abilities to reason about the social implications of everyday events and situations.

A dataset from the Allen Institute of AI consisting of genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset the Challenging Set of questions.

A dataset from the Allen Institute of AI consisting of genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset the Easy Set.

Food 101 Dataset

COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features: