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Computer Science > Computer Vision and Pattern Recognition

arXiv:2403.09611 (cs)
[Submitted on 14 Mar 2024]

Title:MM1: Methods, Analysis & Insights from Multimodal LLM
Pre-training

Authors:Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam
Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang
Peng, Floris Weers, Anton Belyi, Haotian Zhang, Karanjeet Singh, Doug
Kang, Hongyu He, Max Schwarzer, Tom Gunter, Xiang Kong, Aonan Zhang,
Jianyu Wang, Chong Wang, Nan Du, Tao Lei, Sam Wiseman, Mark Lee,
Zirui Wang, Ruoming Pang, Peter Grasch, Alexander Toshev, Yinfei Yang
Download a PDF of the paper titled MM1: Methods, Analysis & Insights
from Multimodal LLM Pre-training, by Brandon McKinzie and 29 other
authors
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    Abstract:In this work, we discuss building performant Multimodal
    Large Language Models (MLLMs). In particular, we study the
    importance of various architecture components and data choices.
    Through careful and comprehensive ablations of the image encoder,
    the vision language connector, and various pre-training data
    choices, we identified several crucial design lessons. For
    example, we demonstrate that for large-scale multimodal
    pre-training using a careful mix of image-caption, interleaved
    image-text, and text-only data is crucial for achieving
    state-of-the-art (SOTA) few-shot results across multiple
    benchmarks, compared to other published pre-training results.
    Further, we show that the image encoder together with image
    resolution and the image token count has substantial impact,
    while the vision-language connector design is of comparatively
    negligible importance. By scaling up the presented recipe, we
    build MM1, a family of multimodal models up to 30B parameters,
    consisting of both dense models and mixture-of-experts (MoE)
    variants, that are SOTA in pre-training metrics and achieve
    competitive performance after supervised fine-tuning on a range
    of established multimodal benchmarks. Thanks to large-scale
    pre-training, MM1 enjoys appealing properties such as enhanced
    in-context learning, and multi-image reasoning, enabling few-shot
    chain-of-thought prompting.

Subjects: Computer Vision and Pattern Recognition (cs.CV);
          Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as:  arXiv:2403.09611 [cs.CV]
          (or arXiv:2403.09611v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2403.09611
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Zhe Gan [view email]
[v1] Thu, 14 Mar 2024 17:51:32 UTC (14,464 KB)
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    Insights from Multimodal LLM Pre-training, by Brandon McKinzie
    and 29 other authors
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