Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions ICLR 2024Spotlights 论文地址 ABSTRACT 许多MLLM用Visual Prompt Generators (VPGs)的方法把图片特征转换为LLM能够理解的token。这种方法用image-caption对进行训练,先把Imgae喂给VPG,然后把VPG生成的tokens喂给LLM产生caption。但是这样的方法...
这里我们以Instruct Tuning为主要展开。首先,这个阶段中可训练的部分包括整个LLM和projection layer。这一阶段可以对应于目前比较火的LLM的Instruction fine tuning,目的是为了让模型更好地遵循用户给出的Instruction。换句话说,为了让模型更好地和人类意图进行对齐。所以,这个阶段的作用可以类比到GPT-3向InstructGPT的转变...
Government Solutions of Digital Divide Data (DDD) includes multimodal labeling, annotation & testing, LLM Fine-Tuning, RLHF & Red Teaming for Defense, Federal, and Public sectors.
微调MLM:在混合指令集上对多模态语言模型进行指令调优。 实验对比数据结果: 在DataComp基准测试中,与CLIPScore相比,MLM过滤器在不同任务子组上显著提高了性能。 在CLIP和BLIP-2模型上,使用MLM过滤器预训练的数据集显著优于使用CLIPScore过滤的数据集。 人类评估显示,MLM过滤器生成的得分与人类评分显著相关,而CLIPScore...
在早期的深度学习或 Transformer 模型中,虽然预训练-微调(pre-training and fine-tuning)范式已经大大减少了标注数据的需求,但对于特定任务仍需要一定量级的标注数据。GPT-3 的出现,进一步减少了这个需求,可能只需要标准几个甚至不需要标注的数据,也可以达到很好的效果。这一发现挑战了以往的认知。
At present Google is still testing and fine-tuning their Gemini multimodal LLM model. Some early testing also speculates that the Gemini model will be more powerful than ChatGPT because it leverages Google data. Meta Meta (previously Facebook) is at work on numerous multimodal AI LLM through ...
MLLM-Finetuning-Demo 安装LLaMA-Factory git clone https://github.com/hiyouga/LLaMA-Factory.gitcdLLaMA-Factory pip install -e .[torch,metrics]cd..#回到项目根目录 预训练 LLaVA中的特征对齐,冻结language_model和vision_tower,只微调multi_modal_projector。
annotations and LLM’s outputs. In addition to the LLM tuning, we also fine-tune the decoding end of NExT-GPT. We align the modal signal token representation encoded by the output projection with the gold multimodal caption representation encoded by the diffusion condition encoder. Thereby, the ...
Multimodal Chain-of-Thought LLM-Aided Visual Reasoning Foundation Models Evaluation Multimodal RLHF Others Awesome Datasets Datasets of Pre-Training for Alignment Datasets of Multimodal Instruction Tuning Datasets of In-Context Learning Benchmarks for Evaluation Others...
5. We introduce three key components: (a) text prompt tuning, (b) multimodal interactive alignment, and (c) CTV delineation. (a) Text prompt tuning To efficiently fine-tune the LLM, we introduce N-text prompts \({{{\mathcal{V}}}=\{{v}^{n}{| }_{n=1}^{N}\}\) as illustrated...