Advanced Embedding Techniques in Multimodal Retrieval Augmented Generation A Comprehensive Study on Cross Modal AI Applications

Ren Zhou

doi:10.54097/h8wf8vah

Authors

Ren Zhou

DOI:

https://doi.org/10.54097/h8wf8vah

Keywords:

Retrieval-Augmented Generation (RAG), Multimodal data, Retrieval accuracy, Generative quality, AI applications

Abstract

Our study presents significant advancements in the handling of multimodal data through an extended Retrieval-Augmented Generation (RAG) model. By integrating advanced embedding techniques, efficient retrieval mechanisms, and robust generative capabilities, our model demonstrates notable improvements in retrieval accuracy, real-time efficiency, generative quality, and scalability. The retrieval accuracy of our model reached 85%, showing a 10% improvement over existing benchmarks. Furthermore, the retrieval time was reduced by 40%, enhancing real-time application performance. The model's generative quality was also significantly improved, with BLEU and ROUGE scores increasing by 15% and 12%, respectively. These results validate the effectiveness of our approach and its applicability to various AI applications, including information retrieval, recommendation systems, and content creation. Future research directions include the integration of additional modalities and further optimization of retrieval mechanisms to broaden the applicability of our model.

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