Retrieval-Augmented Generation (RAG) has significantly advanced the capabilities of large language models (LLMs) by integrating external information retrieval mechanisms. This integration allows models to access and utilize data beyond their original training sets, leading to more accurate and contextually relevant responses. Traditionally, RAG focused on text-based retrieval, enhancing the model's ability to generate informed outputs by accessing up-to-date information. However, recent developments have expanded RAG's scope to include multimodal data, such as images and videos, thereby enriching the model's understanding and response generation. This evolution addresses the limitations of text-only RAG by enabling models to process and generate responses based on a broader range of data types, leading to more nuanced and contextually grounded outputs. arxiv.org
The integration of multimodal retrieval into RAG systems has opened new avenues for applications requiring both visual and textual understanding. For instance, in the field of construction safety management, RAG models have been developed to generate accurate safety information by combining textual guidelines with visual data. These models have demonstrated superior performance in terms of correctness, relevance, and accuracy compared to other GPT models, highlighting the effectiveness of multimodal RAG in specialized domains. By leveraging diverse data sources, RAG systems can provide more comprehensive and contextually relevant responses, thereby enhancing their applicability across various industries and tasks. sciencedirect.com