Recently, there has been a surge of interest in the NLP community on the use of pretrained Language Models (LMs) as Knowledge Bases (KBs). Researchers have shown that LMs trained on a sufficiently large (web) corpus will encode a significant amount of knowledge implicitly in its parameters. The resulting LM can be probed for different kinds of knowledge and thus acting as a KB. This has a major advantage over traditional KBs in that this method requires no human supervision. In this paper, we present a set of aspects that we deem a LM should have to fully act as a KB, and review the recent literature with respect to those aspects.
@article{arxiv.2204.06031,
title = {A Review on Language Models as Knowledge Bases},
author = {Badr AlKhamissi and Millicent Li and Asli Celikyilmaz and Mona Diab and Marjan Ghazvininejad},
journal= {arXiv preprint arXiv:2204.06031},
year = {2022}
}