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Temporal concept drift refers to the problem of data changing over time. InNLP, that would entail that language (e.g. new expressions, meaning shifts) andfactual knowledge (e.g. new concepts, updated facts) evolve over time. Focusingon the latter, we benchmark $11$ pretrained masked language models (MLMs) on aseries of tests designed to evaluate the effect of temporal concept drift, asit is crucial that widely used language models remain up-to-date with theever-evolving factual updates of the real world. Specifically, we provide aholistic framework that (1) dynamically creates temporal test sets of any timegranularity (e.g. month, quarter, year) of factual data from Wikidata, (2)constructs fine-grained splits of tests (e.g. updated, new, unchanged facts) toensure comprehensive analysis, and (3) evaluates MLMs in three distinct ways(single-token probing, multi-token generation, MLM scoring). In contrast toprior work, our framework aims to unveil how robust an MLM is over time andthus to provide a signal in case it has become outdated, by leveraging multipleviews of evaluation.

Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views