增强LLM的愿景:加强LLM的多模式知识存储和共享
概括
我们介绍了MKS2,这是一种通过整合视觉知识来增强大型语言模型 (LLM) 的新方法. MKS2通过使用模块化视觉内存和混合多式联络专家来提高LLM推理和多式联络任务的性能.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 多模式大语言模型 (MLLMs) 主要将视觉数据映射到语言空间,利用LLM的优势来生成.
- 当前的LLM经常忽视视觉知识的潜力,以提高核心的LLM能力,一个概念称为"视觉增强LLM".
研究的目的:
- 通过多模式知识存储和共享提议MKS2,这是一种通过多模式知识存储和共享增强LLM的方法.
- 通过整合视觉信息来改善LLM推理,特别是在物理和常识知识领域.
主要方法:
- 引入集成在LLM内部块中的模块化视觉存储器 (MVM),用于高效的开放世界视觉信息存储.
- 实施软混合多模式专家 (MoMEs) 架构,以促进文本生成期间的多模式知识协作.
主要成果:
- 在需要物理和常识知识的任务中,MKS2显著提高了LLM推理能力.
- 拟议的方法在已建立的图像-文本理解多式联络基准上实现了竞争性表现.
结论:
- MKS2有效地增强了LLM,使他们能够利用视觉知识来改善推理和任务执行.
- MKS2框架为开发更有能力和多功能视觉增强法学士提供了一个有希望的方向.
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