  We propose "Conceptual Coverage Across Languages" (CoCo-CroLa), a technique
for benchmarking the degree to which any generative text-to-image system
provides multilingual parity to its training language in terms of tangible
nouns. For each model we can assess "conceptual coverage" of a given target
language relative to a source language by comparing the population of images
generated for a series of tangible nouns in the source language to the
population of images generated for each noun under translation in the target
language. This technique allows us to estimate how well-suited a model is to a
target language as well as identify model-specific weaknesses, spurious
correlations, and biases without a-priori assumptions. We demonstrate how it
can be used to benchmark T2I models in terms of multilinguality, and how
despite its simplicity it is a good proxy for impressive generalization.
