In recent times, we have seen a rapid development of large Vision-Language Models (VLMs). They have shown impressive results on academic benchmarks, primarily in widely spoken languages but lack performance on low-resource languages and varied cultural contexts. To address these limitations, we introduce Maya, an open-source Multilingual VLM. Our contributions are: 1) a multilingual image-text pretraining dataset in eight languages, based on the LLaVA pretraining dataset; and 2) 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.
Behind Maya: Building a Multilingual Vision Language Model
In recent times, we have seen a rapid development of large Vision-Language Models (VLMs).
- Year
- 2025
- Venue
- arXiv 2025
- Authors
- 19
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2505.08910v2ARXIV-DEFAULT
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