All Languages Matter: Evaluating LMMs on Culturally Diverse 100
  Languages
Existing Large Multimodal Models (LMMs) generally focus on only a few regions
and languages. As LMMs continue to improve, it is increasingly important to
ensure they understand cultural contexts, respect local sensitivities, and
support low-resource languages, all while effectively integrating corresponding
visual cues. In pursuit of culturally diverse global multimodal models, our
proposed All Languages Matter Benchmark (ALM-bench) represents the largest and
most comprehensive effort to date for evaluating LMMs across 100 languages.
ALM-bench challenges existing models by testing their ability to understand and
reason about culturally diverse images paired with text in various languages,
including many low-resource languages traditionally underrepresented in LMM
research. The benchmark offers a robust and nuanced evaluation framework
featuring various question formats, including true/false, multiple choice, and
open-ended questions, which are further divided into short and long-answer
categories. ALM-bench design ensures a comprehensive assessment of a model's
ability to handle varied levels of difficulty in visual and linguistic
reasoning. To capture the rich tapestry of global cultures, ALM-bench carefully
curates content from 13 distinct cultural aspects, ranging from traditions and
rituals to famous personalities and celebrations. Through this, ALM-bench not
only provides a rigorous testing ground for state-of-the-art open and
closed-source LMMs but also highlights the importance of cultural and
linguistic inclusivity, encouraging the development of models that can serve
diverse global populations effectively. Our benchmark is publicly available.
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All Languages Matter: Evaluating LMMs on Culturally Diverse 100
  Languages
Existing Large Multimodal Models (LMMs) generally focus on only a few regions
and languages. As LMMs continue to improve, it is increasingly important to
ensure they understand cultural contexts, respect local sensitivities, and
support low-resource languages, all while effectively integrating corresponding
visual cues. In pursuit of culturally diverse global multimodal models, our
proposed All Languages Matter Benchmark (ALM-bench) represents the largest and
most comprehensive effort to date for evaluating LMMs across 100 languages.
ALM-bench challenges existing models by testing their ability to understand and
reason about culturally diverse images paired with text in various languages,
including many low-resource languages traditionally underrepresented in LMM
research. The benchmark offers a robust and nuanced evaluation framework
featuring various question formats, including true/false, multiple choice, and
open-ended questions, which are further divided into short and long-answer
categories. ALM-bench design ensures a comprehensive assessment of a model's
ability to handle varied levels of difficulty in visual and linguistic
reasoning. To capture the rich tapestry of global cultures, ALM-bench carefully
curates content from 13 distinct cultural aspects, ranging from traditions and
rituals to famous personalities and celebrations. Through this, ALM-bench not
only provides a rigorous testing ground for state-of-the-art open and
closed-source LMMs but also highlights the importance of cultural and
linguistic inclusivity, encouraging the development of models that can serve
diverse global populations effectively. Our benchmark is publicly available.
ALM-bench provides a comprehensive evaluation of Large Multimodal Models across 100 languages, focusing on cultural and linguistic inclusivity.
Large Multimodal Models LMMs ALM-bench culturally diverse images low-resource languages visual and linguistic reasoning true/false questions multiple choice open-ended questions short and long-answer categories cultural aspects traditions rituals famous personalities celebrations global cultures cultural and linguistic inclusivity