title: Ask in Any Modality: A Comprehensive Survey on Multimodal
    Retrieval-Augmented Generation
summary: Large Language Models (LLMs) struggle with hallucinations and outdated
  knowledge due to their reliance on static training data. Retrieval-Augmented
  Generation (RAG) mitigates these issues by integrating external dynamic
  information enhancing factual and updated grounding. Recent advances in
  multimodal learning have led to the development of Multimodal RAG,
  incorporating multiple modalities such as text, images, audio, and video to
  enhance the generated outputs. However, cross-modal alignment and reasoning
  introduce unique challenges to Multimodal RAG, distinguishing it from
  traditional unimodal RAG. This survey offers a structured and comprehensive
  analysis of Multimodal RAG systems, covering datasets, metrics, benchmarks,
  evaluation, methodologies, and innovations in retrieval, fusion, augmentation,
  and generation. We precisely review training strategies, robustness
  enhancements, and loss functions, while also exploring the diverse Multimodal
  RAG scenarios. Furthermore, we discuss open challenges and future research
  directions to support advancements in this evolving field. This survey lays the
  foundation for developing more capable and reliable AI systems that effectively
  leverage multimodal dynamic external knowledge bases. Resources are available
  at https://github.com/llm-lab-org/Multimodal-RAG-Survey.
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paper_authors.name: Bardia Mohammadi
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paper_authors.fullname: Bardia Mohammadi
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paper_authors.name: Omid Ghahroodi
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paper_authors.fullname: Omid Ghahroodi
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paper_authors.name: Mahdieh Soleymani Baghshah
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paper_title: Ask in Any Modality: A Comprehensive Survey on Multimodal
    Retrieval-Augmented Generation
paper_summary: Large Language Models (LLMs) struggle with hallucinations and outdated
  knowledge due to their reliance on static training data. Retrieval-Augmented
  Generation (RAG) mitigates these issues by integrating external dynamic
  information enhancing factual and updated grounding. Recent advances in
  multimodal learning have led to the development of Multimodal RAG,
  incorporating multiple modalities such as text, images, audio, and video to
  enhance the generated outputs. However, cross-modal alignment and reasoning
  introduce unique challenges to Multimodal RAG, distinguishing it from
  traditional unimodal RAG. This survey offers a structured and comprehensive
  analysis of Multimodal RAG systems, covering datasets, metrics, benchmarks,
  evaluation, methodologies, and innovations in retrieval, fusion, augmentation,
  and generation. We precisely review training strategies, robustness
  enhancements, and loss functions, while also exploring the diverse Multimodal
  RAG scenarios. Furthermore, we discuss open challenges and future research
  directions to support advancements in this evolving field. This survey lays the
  foundation for developing more capable and reliable AI systems that effectively
  leverage multimodal dynamic external knowledge bases. Resources are available
  at https://github.com/llm-lab-org/Multimodal-RAG-Survey.
paper_ai_keywords: Retrieval-Augmented Generation (RAG) Multimodal RAG cross-modal alignment cross-modal reasoning datasets metrics benchmarks Evaluation methodologies innovations retrieval fusion augmentation generation training strategies robustness enhancements loss functions diverse Multimodal RAG scenarios open challenges future research directions multimodal dynamic external knowledge bases