🔥 Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders

💡 The paper explores the design space for multimodal large language models that use a mixture of vision encoders and resolutions to improve performance. The goal is to accurately interpret complex visual information, which is crucial for tasks such as optical character recognition and document analysis. Recent work has shown that using multiple vision encoders can enhance visual perception and reduce hallucinations, but there is a lack of systematic comparisons and detailed ablation studies on this topic.

To address this, the authors conducted an extensive exploration of the design space for multimodal large language models using a mixture of vision encoders and resolutions. They found that simply concatenating visual tokens from a set of complementary vision encoders is as effective as more complex mixing architectures or strategies. They also introduced a Pre-Alignment mechanism to bridge the gap between vision-focused encoders and language tokens, which enhances model coherence.

The resulting family of multimodal large language models, called Eagle, surpasses other leading open-source models on major benchmarks. The authors discovered that their streamlined yet effective design approach is based on several underlying principles common to various existing strategies. The Eagle models and code are available online, providing a valuable resource for the research community. Overall, the paper contributes to the development of more effective multimodal large language models by providing a systematic exploration of the design space and introducing a simple yet effective design approach.

📅 Published on Aug 28, 2024

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2408.15998
• PDF: https://arxiv.org/pdf/2408.15998
• Project Page: https://huggingface.co/papers/2407.02392

🤖 Models citing this paper:
• https://huggingface.co/NVEagle/Eagle-X5-13B-Chat
• https://huggingface.co/NVEagle/Eagle-X5-7B
• https://huggingface.co/NVEagle/Eagle-X5-13B

📊 Datasets citing this paper:
• https://huggingface.co/datasets/shi-labs/Eagle-1.8M

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/merve/vision_papers
• https://huggingface.co/spaces/NVEagle/Eagle-X5-13B-Chat
• https://huggingface.co/spaces/shaktibiplab/Eagle-X5-13B-Chat

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📢 By: https://xn--r1a.website/PaperNexus

#MultimodalLLMs #MixtureOfEncoders #VisionEncoderDesign #OpticalCharacterRecognition #DocumentAnalysisModels