Entity Cloze By Date: What LMs Know About Unseen Entities. (arXiv:2205.02832v1 [cs.CL])

Web: http://arxiv.org/abs/2205.02832

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.
We evaluate LMs' perplexity …

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