All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages

TL;DR

This paper introduces ALM-bench, a comprehensive benchmark for evaluating Large Multimodal Models across 100 languages and diverse cultural contexts, emphasizing inclusivity and cultural understanding.

Contribution

It presents the largest and most diverse evaluation framework for LMMs, focusing on cultural and linguistic diversity, including low-resource languages and various cultural aspects.

Findings

LMMs show varying performance across languages and cultures.

The benchmark reveals gaps in models' understanding of low-resource languages.

Cultural diversity significantly impacts model reasoning abilities.

Abstract

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.