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mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration

Qinghao Ye · Haiyang Xu · Jiabo Ye · Ming Yan · Anwen Hu · Haowei Liu · Qi Qian · Ji Zhang · Fei Huang

Arch 4A-E Poster #329
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Thu 20 Jun 10:30 a.m. PDT — noon PDT


Multi-modal Large Language Models (MLLMs) have demonstrated impressive instruction abilities across various open-ended tasks. However, previous methods have primarily focused on enhancing multi-modal capabilities. In this work, we introduce a versatile multi-modal large language model, mPLUG-Owl2, which effectively leverages modality collaboration to improve performance in both text and multi-modal tasks. mPLUG-Owl2 utilizes a modularized network design, with the language decoder acting as a universal interface for managing different modalities. Specifically, mPLUG-Owl2 incorporates shared functional modules to facilitate modality collaboration and introduces a modality-adaptive module that preserves modality-specific features. Extensive experiments reveal that mPLUG-Owl2 is capable of generalizing both text tasks and multi-modal tasks while achieving state-of-the-art performances with a single generalized model. Notably, mPLUG-Owl2 is the first MLLM model that demonstrates the modality collaboration phenomenon in both pure-text and multi-modal scenarios, setting a pioneering path in the development of future multi-modal foundation models.

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