Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries
Benjamin Heinzerling, Kentaro Inui · 2021
Pretrained language models have been suggested as a possible alternative or complement to structured knowledge bases.However, this emerging LM-as-KB paradigm has so far only been considered in a very limited setting, which only allows handling 21k entities whose name is found in common LM vocabularies.Furthermore, a major benefit of this paradigm, i.e., querying the KB using natural language paraphrases, is underexplored.Here we formulate two basic requirements for treating LMs as KBs: (i) the ability to store a large number facts involving a large number of entities and (ii) the ability to query stored facts.We explore three entity representations that allow LMs to handle millions of entities and present a detailed case study on paraphrased querying of facts stored in LMs, thereby providing a proof-of-concept that language models can indeed serve as knowledge bases.