Abstract
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.- Anthology ID:
- 2021.eacl-main.153
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1772–1791
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.153
- DOI:
- 10.18653/v1/2021.eacl-main.153
- Cite (ACL):
- Benjamin Heinzerling and Kentaro Inui. 2021. Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1772–1791, Online. Association for Computational Linguistics.
- Cite (Informal):
- Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries (Heinzerling & Inui, EACL 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.eacl-main.153.pdf
- Code
- bheinzerling/lm-as-kb