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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Fabio Petroni
- sl:arxiv_num : 1909.01066
- sl:arxiv_published : 2019-09-03T11:11:08Z
- sl:arxiv_summary : Recent progress in pretraining language models on large textual corpora led
to a surge of improvements for downstream NLP tasks. Whilst learning linguistic
knowledge, these models may also be storing relational knowledge present in the
training data, and may be able to answer queries structured as
\"fill-in-the-blank\" cloze statements. Language models have many advantages over
structured knowledge bases: they require no schema engineering, allow
practitioners to query about an open class of relations, are easy to extend to
more data, and require no human supervision to train. We present an in-depth
analysis of the relational knowledge already present (without fine-tuning) in a
wide range of state-of-the-art pretrained language models. We find that (i)
without fine-tuning, BERT contains relational knowledge competitive with
traditional NLP methods that have some access to oracle knowledge, (ii) BERT
also does remarkably well on open-domain question answering against a
supervised baseline, and (iii) certain types of factual knowledge are learned
much more readily than others by standard language model pretraining
approaches. The surprisingly strong ability of these models to recall factual
knowledge without any fine-tuning demonstrates their potential as unsupervised
open-domain QA systems. The code to reproduce our analysis is available at
https://github.com/facebookresearch/LAMA.@en
- sl:arxiv_title : Language Models as Knowledge Bases?@en
- sl:arxiv_updated : 2019-09-04T09:33:20Z
- sl:bookmarkOf : https://arxiv.org/abs/1909.01066
- sl:creationDate : 2019-09-05
- sl:creationTime : 2019-09-05T22:32:00Z
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