  Challenging problems such as open-domain question answering, fact checking,
slot filling and entity linking require access to large, external knowledge
sources. While some models do well on individual tasks, developing general
models is difficult as each task might require computationally expensive
indexing of custom knowledge sources, in addition to dedicated infrastructure.
To catalyze research on models that condition on specific information in large
textual resources, we present a benchmark for knowledge-intensive language
tasks (KILT). All tasks in KILT are grounded in the same snapshot of Wikipedia,
reducing engineering turnaround through the re-use of components, as well as
accelerating research into task-agnostic memory architectures. We test both
task-specific and general baselines, evaluating downstream performance in
addition to the ability of the models to provide provenance. We find that a
shared dense vector index coupled with a seq2seq model is a strong baseline,
outperforming more tailor-made approaches for fact checking, open-domain
question answering and dialogue, and yielding competitive results on entity
linking and slot filling, by generating disambiguated text. KILT data and code
are available at https://github.com/facebookresearch/KILT.
