content.raw: 🌍 I’ve always had a dream of making AI accessible to everyone, regardless of location or language. However, current open MLLMs often respond in English, even to non-English queries!
content.type: text
content.value: 🌍 I’ve always had a dream of making AI accessible to everyone, regardless of location or language. However, current open MLLMs often respond in English, even to non-English queries!
content.type: new_line
content.type: new_line
content.raw: 🚀 Introducing Pangea: A Fully Open Multilingual Multimodal LLM supporting 39 languages! 🌐✨
content.type: text
content.value: 🚀 Introducing Pangea: A Fully Open Multilingual Multimodal LLM supporting 39 languages! 🌐✨
content.type: new_line
content.type: new_line
content.href: https://neulab.github.io/Pangea/
content.raw: https://neulab.github.io/Pangea/
content.type: link
content.type: new_line
content.href: https://arxiv.org/pdf/2410.16153
content.raw: https://arxiv.org/pdf/2410.16153
content.type: link
content.type: new_line
content.type: new_line
content.raw: The Pangea family includes three major components:
content.type: text
content.value: The Pangea family includes three major components:
content.type: new_line
content.raw: 🔥 Pangea-7B: A state-of-the-art multilingual multimodal LLM capable of 39 languages! Not only does it excel in multilingual scenarios, but it also matches or surpasses English-centric models like Llama 3.2, Molmo, and LlavaOneVision in English performance.
content.type: text
content.value: 🔥 Pangea-7B: A state-of-the-art multilingual multimodal LLM capable of 39 languages! Not only does it excel in multilingual scenarios, but it also matches or surpasses English-centric models like Llama 3.2, Molmo, and LlavaOneVision in English performance.
content.type: new_line
content.type: new_line
content.raw: 📝 PangeaIns: A 6M multilingual multimodal instruction tuning dataset across 39 languages. 🗂️ With 40% English instructions and 60% multilingual instructions, it spans various domains, including 1M culturally-relevant images sourced from LAION-Multi. 🎨
content.type: text
content.value: 📝 PangeaIns: A 6M multilingual multimodal instruction tuning dataset across 39 languages. 🗂️ With 40% English instructions and 60% multilingual instructions, it spans various domains, including 1M culturally-relevant images sourced from LAION-Multi. 🎨
content.type: new_line
content.type: new_line
content.raw: 🏆 PangeaBench: A comprehensive evaluation benchmark featuring 14 datasets in 47 languages. Evaluation can be tricky, so we carefully curated existing benchmarks and introduced two new datasets: xChatBench (human-annotated wild queries with fine-grained evaluation criteria) and xMMMU (a meticulously machine-translated version of MMMU).
content.type: text
content.value: 🏆 PangeaBench: A comprehensive evaluation benchmark featuring 14 datasets in 47 languages. Evaluation can be tricky, so we carefully curated existing benchmarks and introduced two new datasets: xChatBench (human-annotated wild queries with fine-grained evaluation criteria) and xMMMU (a meticulously machine-translated version of MMMU).
content.type: new_line
content.type: new_line
content.raw: Check out more details: 
content.type: text
content.value: Check out more details: 
content.href: https://x.com/xiangyue96/status/1848753709787795679
content.raw: https://x.com/xiangyue96/status/1848753709787795679
content.type: link
rawContent: 🌍 I’ve always had a dream of making AI accessible to everyone, regardless of location or language. However, current open MLLMs often respond in English, even to non-English queries!
  
  🚀 Introducing Pangea: A Fully Open Multilingual Multimodal LLM supporting 39 languages! 🌐✨
  
  https://neulab.github.io/Pangea/
  https://arxiv.org/pdf/2410.16153
  
  The Pangea family includes three major components:
  🔥 Pangea-7B: A state-of-the-art multilingual multimodal LLM capable of 39 languages! Not only does it excel in multilingual scenarios, but it also matches or surpasses English-centric models like Llama 3.2, Molmo, and LlavaOneVision in English performance.
  
  📝 PangeaIns: A 6M multilingual multimodal instruction tuning dataset across 39 languages. 🗂️ With 40% English instructions and 60% multilingual instructions, it spans various domains, including 1M culturally-relevant images sourced from LAION-Multi. 🎨
  
  🏆 PangeaBench: A comprehensive evaluation benchmark featuring 14 datasets in 47 languages. Evaluation can be tricky, so we carefully curated existing benchmarks and introduced two new datasets: xChatBench (human-annotated wild queries with fine-grained evaluation criteria) and xMMMU (a meticulously machine-translated version of MMMU).
  
  Check out more details: https://x.com/xiangyue96/status/1848753709787795679
reactions.reaction: 🚀
reactions.users: AdinaY John6666 victor apol Felladrin oceansweep KhangHatto nbroad oumayma03
reactions.reaction: 🔥
reactions.users: AdinaY KhangHatto theospeak