Research Area:  Machine Learning

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 models 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.

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

Author(s) Name:  Zhou, R

Publisher name:  Darcy & Roy Press

DOI:  10.54097/h8wf8vah

Volume Information:  Volume 13,(2024)

Paper Link:   https://drpress.org/ojs/index.php/jceim/article/view/24094