Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning

• URL: http://arxiv.org/abs/2312.12379v5
• Date: Thu, 4 Jul 2024 02:55:57 GMT
• Title: Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning
• Authors: Yunhao Gou, Zhili Liu, Kai Chen, Lanqing Hong, Hang Xu, Aoxue Li, Dit-Yan Yeung, James T. Kwok, Yu Zhang,
• Abstract summary: Mixture of Cluster-conditional LoRA Experts (MoCLE) MoCLE is a novel Mixture of Experts architecture designed to activate the task-customized model parameters based on the instruction clusters. Experiments on InstructBLIP and LLaVA demonstrate the effectiveness of MoCLE.
• Score: 68.94230363140771
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instruction tuning of Large Vision-language Models (LVLMs) has revolutionized the development of versatile models with zero-shot generalization across a wide range of downstream vision-language tasks. However, the diversity of training tasks of different sources and formats would lead to inevitable task conflicts, where different tasks conflict for the same set of model parameters, resulting in sub-optimal instruction-following abilities. To address that, we propose the Mixture of Cluster-conditional LoRA Experts (MoCLE), a novel Mixture of Experts (MoE) architecture designed to activate the task-customized model parameters based on the instruction clusters. A separate universal expert is further incorporated to improve generalization capabilities of MoCLE for novel instructions. Extensive experiments on InstructBLIP and LLaVA demonstrate the effectiveness of MoCLE.

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