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

7 Nov 2023  ·  Qinghao Ye, Haiyang Xu, Jiabo Ye, Ming Yan, Anwen Hu, Haowei Liu, Qi Qian, Ji Zhang, Fei Huang, Jingren Zhou ·

Multi-modal Large Language Models (MLLMs) have demonstrated impressive instruction abilities across various open-ended tasks. However, previous methods primarily focus 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 and achieving state-of-the-art performances with a single generic 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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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Visual Question Answering (VQA) InfiMM-Eval mPLUG-Owl2 Overall score 20.05 # 11
Deductive 23.43 # 10
Abductive 20.6 # 11
Analogical 7.64 # 11
Params 7B # 1
Visual Question Answering MM-Vet mPLUG-Owl2 GPT-4 score 36.3±0.1 # 47
Params 7B # 1