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Towards Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs

2023-11-27 12:29:20
Yunxin Li, Baotian Hu, Wei Wang, Xiaochun Cao, Min Zhang


Recent advancements in multimodal large language models (MLLMs) have achieved significant multimodal generation capabilities, akin to GPT-4. These models predominantly map visual information into language representation space, leveraging the vast knowledge and powerful text generation abilities of LLMs to produce multimodal instruction-following responses. We could term this method as LLMs for Vision because of its employing LLMs for visual-language understanding, yet observe that these MLLMs neglect the potential of harnessing visual knowledge to enhance overall capabilities of LLMs, which could be regraded as Vision Enhancing LLMs. In this paper, we propose an approach called MKS2, aimed at enhancing LLMs through empowering Multimodal Knowledge Storage and Sharing in LLMs. Specifically, we introduce the Modular Visual Memory, a component integrated into the internal blocks of LLMs, designed to store open-world visual information efficiently. Additionally, we present a soft Mixtures-of-Multimodal Experts architecture in LLMs to invoke multimodal knowledge collaboration during generation. Our comprehensive experiments demonstrate that MKS2 substantially augments the reasoning capabilities of LLMs in contexts necessitating physical or commonsense knowledge. It also delivers competitive results on multimodal benchmarks.

Abstract (translated)

近年来,在多模态大型语言模型(MLLMs)方面的进步已经取得了显著的多模态生成能力,与GPT-4相当。这些模型主要将视觉信息映射到语言表示空间,利用LLM的广泛知识和强大的文本生成能力来产生多模态遵循指令的响应。因此,我们可以称这种方法为LLMs for Vision,因为它利用LLMs进行视觉-语言理解。然而,我们观察到,这些MLLMs忽视了利用视觉知识增强LLM总体能力的机会,这可能被视为Vision Enhancing LLMs。在本文中,我们提出了MKS2,旨在通过在LLMs中增强多模态知识存储和共享来提高LLMs。具体来说,我们引入了模块化视觉内存,这是一个集成在LLM内部块中的组件,旨在高效地存储开放世界视觉信息。此外,我们在LLMs中提出了软多模态专家架构,用于在生成过程中激发多模态知识合作。我们的全面实验证明,MKS2极大地增强了在需要物理或常识知识的情境下LLMs的推理能力。此外,在多模态基准测试中,MKS2还取得了竞争力的结果。



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