A Review on Language Models as Knowledge Bases

Autor:	AlKhamissi, Badr, Li, Millicent, Celikyilmaz, Asli, Diab, Mona, Ghazvininejad, Marjan
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: Preprint
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.06031 Zobrazit plný text záznamu View this record from Arxiv