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MS MARCO NLGen
The MS MARCO NLGen dataset is a collection of natural language generation tasks, where the goal is to generate natural-sounding answers to questions. -
FactCheckQA
FactCheckQA is a refreshable dataset for probing model performance in trusted source alignment. -
SimpleQuestion Dataset
The dataset used in the paper is a collection of data for the Simple Question dataset, which contains questions answerable using Wikidata as the knowledge graph. -
Collective classification in network data
Collective classification in network data. -
GeoQA and GeoQA+
Geometry Problem Solving (GPS), which is a classic and challenging math problem, has attracted much attention in recent years. It requires a solver to comprehensively understand... -
CommonsenseQA
The dataset used in the paper is also mentioned as CommonsenseQA, which is a 5-way multiple choice QA dataset that requires commonsense knowledge. -
Natural Questions
The Natural Questions dataset consists of questions extracted from web queries, with each question accompanied by a corresponding Wikipedia article containing the answer. -
Contextualized Sequence Likelihood
The authors used several question-answering datasets, including CoQA, TriviaQA, and Natural Questions. -
EndoVis-17-VQLA
EndoVis-17-VQLA dataset is a public dataset with 97 frames with common tools and interactions from EndoVis-2017. It is annotated with question-answer-bounding box labels. -
EndoVis-18-VQLA
EndoVis-18-VQLA dataset is a public dataset with 14 video sequences on robotics surgery procedures. It is combined with the bounding box on tissue-instrument interaction... -
FUNSD dataset
FUNSD dataset contains questions answerable using Wikidata as the knowledge graph, focusing on questions with a single entity and relation. -
CORD dataset
CORD dataset contains questions answerable using Wikidata as the knowledge graph, focusing on questions with a single entity and relation. -
Visual7W dataset
The Visual7W dataset is a visual question answering dataset, which consists of images and corresponding questions.