Maya: An Instruction Finetuned Multilingual Multimodal Model
Authors: name: Nahid Alam, name: Karthik Reddy Kanjula, name: Surya Guthikonda, name: Timothy Chung, name: Bala Krishna S Vegesna, name: Abhipsha Das, name: Anthony Susevski, name: Ryan Sze-Yin Chan, name: S M Iftekhar Uddin, name: Shayekh Bin Islam, name: Roshan Santhosh, name: Snegha A, name: Drishti Sharma, name: Chen Liu, name: Isha Chaturvedi, name: Genta Indra Winata, name: Ashvanth. S, name: Snehanshu Mukherjee, name: Alham Fikri Aji

The rapid development of large Vision-Language Models (VLMs) has led to
impressive results on academic benchmarks, primarily in widely spoken
languages. However, significant gaps remain in the ability of current VLMs to
handle low-resource languages and varied cultural contexts, largely due to a
lack of high-quality, diverse, and safety-vetted data. Consequently, these
models often struggle to understand low-resource languages and cultural nuances
in a manner free from toxicity. To address these limitations, we introduce
Maya, an open-source Multimodal Multilingual model. Our contributions are
threefold: 1) a multilingual image-text pretraining dataset in eight languages,
based on the LLaVA pretraining dataset; 2) a thorough analysis of toxicity
within the LLaVA dataset, followed by the creation of a novel toxicity-free
version across eight languages; and 3) a multilingual image-text model
supporting these languages, enhancing cultural and linguistic comprehension in
vision-language tasks. Code available at https://github.com/nahidalam/maya.