KILT: a Benchmark for Knowledge Intensive Language Tasks
This work addresses the challenge of developing general models for knowledge-intensive NLP tasks by providing a unified benchmark, though it is incremental as it builds on existing methods for benchmarking and model evaluation.
The authors introduced KILT, a benchmark for knowledge-intensive language tasks grounded in a single Wikipedia snapshot, to facilitate research into general models that condition on large textual resources. They found that a shared dense vector index with a seq2seq model outperformed task-specific approaches in tasks like fact checking and question answering, achieving competitive results in entity linking and slot filling.
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.