Tag: Multimodal Data
All the articles with the tag "Multimodal Data".
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Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs
本文提出UniME框架,通过文本判别知识蒸馏和硬负例增强指令微调,利用多模态大语言模型学习通用的多模态嵌入,提高了下游任务的判别性和组合能力。
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Training Plug-n-Play Knowledge Modules with Deep Context Distillation
本文提出使用深度上下文蒸馏训练可插拔知识模块的方法,能够在低数据场景下高效整合文档知识,并通过实验证明其在问答任务中优于传统方法且与 RAG 具有协同效应。
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Exploring the Role of Diversity in Example Selection for In-Context Learning
本文提出基于多样性的上下文学习(DICL)方法,通过最大边际相关性(MMR)算法重新排序示例以平衡相关性和多样性,在多个数据集和大型语言模型上实现了约70%的下游任务性能提升或维持。
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Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders
This paper uses Sparse Autoencoders to identify and manipulate language-specific features in Large Language Models, introducing a monolinguality metric, demonstrating context dependency via code-switching, and enhancing steering vectors for better control over multilingual generation while revealing significant language-specific impacts through ablation studies.
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Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision
本文提出Instruct-LF方法,通过结合LLMs的指令遵循能力和梯度-based统计模型,实现无需任务监督的目标导向潜在因素发现,提高了下游任务性能并在人工评估中被偏好。