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All these datasets have been grounded in a single pre-processed wikipedia dump, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning with minimal effort.
KILT also provides tools to analyze and understand the predictions made by models, as well as the evidence they provide for their predictions. If using these datasets, please cite the paper associated with the KILT datasets original papers. Knowledge-intensive tasks — those where a human can only be expected to perform the task with access to a knowledge source such as wikipedia — are challenging for even cutting edge NLP models.
These tasks require models to precisely recall, manipulate and apply knowledge about the world as well as provide evidence and justifications for their predictions.
These tasks are often studied in isolation and require a lot of development effort to work. KILT is resource designed to make working on knowledge intensive tasks easier, more vigorous and more reproducible.