本论文旨在追踪和总结多模态大语言模型(Multimodal Large Language Model)的最新进展,主要内容包括模型架构、训练策略和数据以及评估。然后,作者介绍了关于如何扩展多模态大语言模型以支持更多粒度、模态、语言和场景的研究主题。作者还介绍了多模态大语言模型面临的幻觉问题以及包括多模态上下文学习、多模态思维链、大语言模...
GitHub - BradyFU/Awesome-Multimodal-Large-Language-Models: :sparkles::sparkles:Latest Papers and Datasets on Multimodal Large Language Models, and Their Evaluation.github.com/BradyFU/Awesome-Multimodal-Large-Language-Models 现在LLM已经广泛用到了多模态方法中,基于LLM的强大智能来完成复杂的多模态任务。...
综述一:A Survey on Multimodal Large Language Models 论文链接:https://arxiv.org/pdf/2306.13549.pdf 项目链接:https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models 2024年4月1号更新的一篇paper。 一、多模态LLM的组成部分 常见的多模态LLM结构: 对于多模态输入-文本输出的典型 MLLM,其架构...
This survey presents a comprehensive analysis of the phenomenon of hallucination in multimodal large language models (MLLMs), also known as Large Vision-Language Models (LVLMs), which have demonstrated significant advancements and remarkable abilities in multimodal tasks. Despite these promising development...
The first survey for Multimodal Large Language Models (MLLMs). ✨ Welcome to add WeChat ID (wmd_ustc) to join our MLLM communication group! 🌟 🔥🔥🔥MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models ...
📌 What is This Survey About? In the past year, Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning. However, the extensive model size and high training and inference costs have hindered the...
摘要原文 In the past year, MultiModal Large Language Models (MM-LLMs) have undergone substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs via cost-effective training strategies. The resulting models not only preserve the inherent reasoning and decision-making capab...
As shown in Fig.1, we develop aMedical Multimodal Large Language Model(Med-MLLM) for rare diseases to deal with the situation where the labelled data is scarce. An example is the early stages of a new pandemic, for which we will have very little data. Med-MLLM (i) adopts the unlabel...
5月17日,鹅厂协同国内几大高校实验室发布了一篇有关多模态大模型的综述文章《Efficient Multimodal Large Language Models: A Survey》,有广度有深度地介绍了多模态大模型的行业发展现状,对多模态大模型发展感觉兴趣的同学觉得有用就一键三连吧~ *本文只摘译精华部分,需要了解全文的请至文末跳转至原文链接阅读。
多模态大模型综述(二):A Survey on Multimodal Large Language Models--训练策略与数据 3.训练策略与数据 一个完整的MLLM经历三个阶段的训练,即预训练、指令微调和对齐微调。训练的每个阶段都需要不同类型的数据,并实现不同的目标。在本节中,我们将讨论训练目标,以及每个训练阶段的数据收集和特征。