Maya: An Instruction Finetuned Multilingual Multimodal Model
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.
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Nahid Alam
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Karthik Reddy Kanjula
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Surya Guthikonda
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Timothy Chung
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Anthony Susevski
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Anthony Susevski
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Ryan Sze-Yin Chan
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S M Iftekhar Uddin
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Snegha A
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Drishti Sharma
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Chen Liu
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Snehanshu Mukherjee
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Alham Fikri Aji
Maya: An Instruction Finetuned Multilingual Multimodal Model
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.
Maya is an open-source multimodal multilingual model that addresses the limitations of Vision-Language Models in handling low-resource languages and cultural nuances by introducing a multilingual pretraining dataset and analyzing/removing toxicity.
Vision-Language Models VLMs multilingual image-text pretraining dataset LLaVA toxicity-free dataset multilingual image-text model cultural comprehension linguistic comprehension