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Otter: A Multi-Modal Model With In-Context Instruction Tuning.

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    We introduce Otter, a new Large Multimodal Model (LMM) that uses both text and images as in-context examples for instruction tuning. This approach significantly improves the model's ability to understand and follow complex multimodal instructions.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Natural Language Processing

    Background:

    • Large Multimodal Models (LMMs) show promise as visual assistants.
    • Existing LMMs primarily focus on single instructions or dialogue history, neglecting multimodal in-context examples.
    • There's a need to enhance instruction-following capabilities using both visual and textual in-context learning.

    Purpose of the Study:

    • To introduce the Otter model, designed for general-purpose multimodal assistance.
    • To leverage both textual and visual in-context examples for instruction tuning LMMs.
    • To improve the instruction-following capabilities of LMMs.

    Main Methods:

    • Otter is built upon the Flamingo architecture with a Perceiver architecture.
    • The model is instruction-tuned using the novel MIMIC-IT dataset.
    • MIMIC-IT contains over 3 million multimodal instruction-response pairs with diverse in-context examples.

    Main Results:

    • Instruction tuning with in-context examples significantly enhances model convergence and generalization.
    • Otter seamlessly processes multimodal inputs, including text, multiple images, and video.
    • The MIMIC-IT dataset enables Otter to excel in complex video and multi-image understanding tasks.

    Conclusions:

    • Multimodal in-context learning is crucial for advancing LMM capabilities.
    • The Otter model and MIMIC-IT dataset represent a significant step towards more capable multimodal assistants.
    • Future work can explore further enhancements in multimodal instruction following and understanding.