KILT: a Benchmark for Knowledge Intensive Language Tasks

TL;DR

KILT introduces a unified benchmark for knowledge-intensive language tasks using a shared Wikipedia snapshot, enabling fair comparison of models and fostering development of general, memory-augmented NLP systems.

Contribution

The paper presents KILT, a comprehensive benchmark for multiple knowledge-intensive tasks based on a common data source, facilitating research on general models and memory architectures.

Findings

Shared dense vector index with seq2seq models performs well across tasks.

Models can effectively provide provenance information.

Competitive results achieved on multiple knowledge tasks.

Abstract

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.