Entity Cloze By Date: What LMs Know About Unseen Entities

Understand

Language models (LMs) are typically trained once on a large-scale corpus and used for years without being updated.

• However, in a dynamic world, new entities constantly arise.
• We propose a framework to analyze what LMs can infer about new entities that did not exist when the LMs were pretrained.
• We derive a dataset of entities indexed by their origination date and paired with their English Wikipedia articles, from which we can find sentences about each entity.