MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale
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2412.05237
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46
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wenhu
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yuexiang96
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JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation
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2410.17250
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14
Long Context Alignment with Short Instructions and Synthesized Positions
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2405.03939
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LIME: Less Is More for MLLM Evaluation
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2409.06851
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Machine Unlearning of Pre-trained Large Language Models
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2402.15159
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aaabiao
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yuexiang96
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luodian
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yuexiang96
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wenhu
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yuexiang96
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🌍 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
🚀 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
yuexiang96
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2 months ago
luodian
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2 months ago
aaabiao
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DEEP-ICL: Definition-Enriched Experts for Language Model In-Context Learning
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2403.04233
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Published
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1
CodeEditorBench: Evaluating Code Editing Capability of Large Language Models
Paper
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2404.03543
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Published
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15
Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model
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2404.04167
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Published
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12