Poster

All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages

Ashmal Vayani ⋅ Dinura Dissanayake ⋅ Hasindri Watawana ⋅ Noor Ahsan ⋅ Nevasini Sasikumar ⋅ Omkar Thawakar ⋅ Henok Biadglign Ademtew ⋅ Yahya Hmaiti ⋅ Amandeep Kumar ⋅ Kartik Kuckreja ⋅ Mykola Maslych ⋅ Wafa Al Ghallabi ⋅ Mihail Minkov Mihaylov ⋅ Chao Qin ⋅ Abdelrahman Shaker ⋅ Mike Zhang ⋅ Mahardika Krisna Ihsani ⋅ Amiel Gian Esplana ⋅ Monil Gokani ⋅ Shachar Mirkin ⋅ Harsh Singh ⋅ Ashay Srivastava ⋅ Endre Hamerlik ⋅ Fathinah Asma Izzati ⋅ Fadillah Adamsyah Maani ⋅ Sebastian Cavada ⋅ Jenny Chim ⋅ Rohit Gupta ⋅ Sanjay Manjunath ⋅ Kamila Zhumakhanova ⋅ Feno Heriniaina Rabevohitra ⋅ Azril Hafizi Amirudin ⋅ Muhammad Ridzuan ⋅ Daniya Najiha Abdul Kareem ⋅ Ketan Pravin More ⋅ Kunyang Li ⋅ Pramesh Shakya ⋅ Muhammad Saad ⋅ Amirpouya Ghasemaghaei ⋅ Amirbek Djanibekov ⋅ Dilshod Azizov ⋅ Branislava Jankovic ⋅ Naman Bhatia ⋅ Alvaro Cabrera Berobide ⋅ Johan Obando-Ceron ⋅ Olympiah Otieno ⋅ Fabian Farestam ⋅ Muztoba Rabbani ⋅ Sanoojan Baliah ⋅ Santosh Sanjeev ⋅ Abduragim Shtanchaev ⋅ Maheen Fatima ⋅ Thao Nguyen ⋅ Amrin Kareem ⋅ Toluwani Aremu ⋅ Nathan Augusto Zacarias Xavier ⋅ Amit Bhatkal ⋅ Hawau Olamide Toyin ⋅ Aman Chadha ⋅ Hisham Cholakkal ⋅ Rao Anwer ⋅ Michael Felsberg ⋅ Jorma Laaksonen ⋅ Thamar Solorio ⋅ Monojit Choudhury ⋅ Ivan Laptev ⋅ Mubarak Shah ⋅ Salman Khan ⋅ Fahad Shahbaz Khan

Highlight

2025 Poster

Paper PDF [ Poster]

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 multimodal 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 will be publicly released.

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