A Survey of Multimodal Retrieval-Augmented Generation

Understand

Multimodal Retrieval-Augmented Generation (MRAG) enhances large language models (LLMs) by integrating multimodal data (text, images, videos) into retrieval and generation processes, overcoming the limitations of text-only Retrieval-Augmented Generation (RAG).

• While RAG improves response accuracy by incorporating external textual knowledge, MRAG extends this framework to include multimodal retrieval and generation, leveraging contextual information from diverse data types.
• This approach reduces hallucinations and enhances question-answering systems by grounding responses in factual, multimodal knowledge.
• Recent studies show MRAG outperforms traditional RAG, especially in scenarios requiring both visual and textual understanding.