title: Maya: An Instruction Finetuned Multilingual Multimodal Model
summary: 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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paper_authors.name: Snehanshu Mukherjee
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paper_authors.name: Alham Fikri Aji
paper_title: Maya: An Instruction Finetuned Multilingual Multimodal Model
paper_summary: 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.
paper_ai_keywords: Vision-Language Models (VLMs) Multimodal Multilingual model multilingual image-text pretraining dataset LLaVA pretraining dataset toxicity analysis toxicity-free version multilingual image-text model cultural and linguistic comprehension