  In pursuit of more inclusive Vision-Language Models (VLMs), this study
introduces a Large Multilingual Multimodal Model called PALO. PALO offers
visual reasoning capabilities in 10 major languages, including English,
Chinese, Hindi, Spanish, French, Arabic, Bengali, Russian, Urdu, and Japanese,
that span a total of ~5B people (65% of the world population). Our approach
involves a semi-automated translation approach to adapt the multimodal
instruction dataset from English to the target languages using a fine-tuned
Large Language Model, thereby ensuring high linguistic fidelity while allowing
scalability due to minimal manual effort. The incorporation of diverse
instruction sets helps us boost overall performance across multiple languages
especially those that are underrepresented like Hindi, Arabic, Bengali, and
Urdu. The resulting models are trained across three scales (1.7B, 7B and 13B
parameters) to show the generalization and scalability where we observe
substantial improvements compared to strong baselines. We also propose the
first multilingual multimodal benchmark for the forthcoming approaches to
evaluate their vision-language reasoning capabilities across languages. Code:
https://github.com/mbzuai-oryx/PALO.
