MVL-SIB: A Massively Multilingual Vision-Language Benchmark for
  Cross-Modal Topical Matching
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.
67b5b31f5a17526b55c3ccde
Fabian David Schmidt
claimed_verified
2025-02-20T15:53:15.852Z
64c8c2d87d0ea4e7f12995c6
https://cdn-avatars.huggingface.co/v1/production/uploads/64c8c2d87d0ea4e7f12995c6/h8eWJrz8kqavemy8vQ2NK.jpeg
Fabian David Schmidt
user
fdschmidt93
67b5b31f5a17526b55c3ccdf
Florian Schneider
claimed_verified
2025-02-20T10:49:49.711Z
62dfd54798815401141c47fe
https://cdn-avatars.huggingface.co/v1/production/uploads/62dfd54798815401141c47fe/ct2OA_K0Wwpshy8DCswxy.png
Flo Schneider
user
floschne
67b5b31f5a17526b55c3cce0
Chris Biemann
67b5b31f5a17526b55c3cce1
Goran Glavaš
MVL-SIB: A Massively Multilingual Vision-Language Benchmark for
  Cross-Modal Topical Matching
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.
MVL-SIB, a multilingual vision-language benchmark spanning 205 languages, reveals that existing LVLMs underperform in cross-modal topic matching for low-resource languages.
multilingual vision-language benchmarks LVLMs cross-modal matching text-only matching GPT-4o(-mini) lower-resource languages N'Koo VL support textual support multi-image tasks