MVL-SIB: A Massively Multilingual Vision-Language Benchmark for
  Cross-Modal Topical Matching
Authors: name: Fabian David Schmidt, name: Florian Schneider, name: Chris Biemann, name: Goran Glavaš

Existing multilingual vision-language (VL) benchmarks often only cover a
handful of languages. Consequently, evaluations of large vision-language models
(LVLMs) predominantly target high-resource languages, underscoring the need for
evaluation data for low-resource languages. To address this limitation, we
introduce MVL-SIB, a massively multilingual vision-language benchmark that
evaluates both cross-modal and text-only topical matching across 205 languages
-- over 100 more than the most multilingual existing VL benchmarks encompass.
We then benchmark a range of of open-weight LVLMs together with GPT-4o(-mini)
on MVL-SIB. Our results reveal that LVLMs struggle in cross-modal topic
matching in lower-resource languages, performing no better than chance on
languages like N'Koo. Our analysis further reveals that VL support in LVLMs
declines disproportionately relative to textual support for lower-resource
languages, as evidenced by comparison of cross-modal and text-only topical
matching performance. We further observe that open-weight LVLMs do not benefit
from representing a topic with more than one image, suggesting that these
models are not yet fully effective at handling multi-image tasks. By
correlating performance on MVL-SIB with other multilingual VL benchmarks, we
highlight that MVL-SIB serves as a comprehensive probe of multilingual VL
understanding in LVLMs.