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

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.

References

Bruckhaus, T. (2024). RAG Does Not Work for Enterprises. arXiv preprint arXiv:2406.04369.

Yao, Y. (2024). Application of Artificial Intelligence in Smart Cities: Current Status, Challenges and Future Trends. International Journal of Computer Science and Information Technology, 2(2), 324-333.

Yao, Y. (2024). Digital Government Information Platform Construction: Technology, Challenges and Prospects. International Journal of Social Sciences and Public Administration, 2(3), 48-56.

Sachan, D. S., Lewis, M., Yogatama, D., Zettlemoyer, L., Pineau, J., & Zaheer, M. (2023). Questions are all you need to train a dense passage retriever. Transactions of the Association for Computational Linguistics, 11, 600-616.

Xu, T. (2024). Comparative Analysis of Machine Learning Algorithms for Consumer Credit Risk Assessment. Transactions on Computer Science and Intelligent Systems Research, 4, 60-67.

Kim, K., & Lee, J. Y. (2024). RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation. arXiv preprint arXiv:2406.05794.

Lian, J., & Chen, T. (2024). Research on Complex Data Mining Analysis and Pattern Recognition Based on Deep Learning. Journal of Computing and Electronic Information Management, 12(3), 37-41.

Pan, X., Ye, T., Han, D., Song, S., & Huang, G. (2022). Contrastive language-image pre-training with knowledge graphs. Advances in Neural Information Processing Systems, 35, 22895-22910.

Yao, Y. (2022). A Review of the Comprehensive Application of Big Data, Artificial Intelligence, and Internet of Things Technologies in Smart Cities. Journal of Computational Methods in Engineering Applications, 1-10.

Wang, S., Zhao, H., & Li, K. (2022). Discrete joint semantic alignment hashing for cross-modal image-text search. IEEE Transactions on Circuits and Systems for Video Technology, 32(11), 8022-8036.

Yang, Y., Guo, Z., Gellman, A. J., & Kitchin, J. R. (2022). Simulating segregation in a ternary Cu–Pd–Au alloy with density functional theory, machine learning, and Monte Carlo simulations. The Journal of Physical Chemistry C, 126(4), 1800-1808.

Yang, Y., Liu, M., & Kitchin, J. R. (2022). Neural network embeddings based similarity search method for atomistic systems. Digital Discovery, 1(5), 636-644.

Yang, Y., Achar, S. K., & Kitchin, J. R. (2022). Evaluation of the degree of rate control via automatic differentiation. AIChE Journal, 68(6), e17653.

Yang, Y., Guo, Z., Gellman, A. J., & Kitchin, J. (2022, November). Modeling Ternary Alloy Segregation with Density Functional Theory and Machine Learning. In 2022 AIChE Annual Meeting. AIChE.

Jin, Y., Li, Y., Yuan, Z., & Mu, Y. (2023). Learning instance-level representation for large-scale multi-modal pretraining in e-commerce. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11060-11069).

Lin, Y. (2023). Construction of Computer Network Security System in the Era of Big Data. Advances in Computer and Communication, 4(3).

Binte Rashid, M., Rahaman, M. S., & Rivas, P. (2024). Navigating the Multimodal Landscape: A Review on Integration of Text and Image Data in Machine Learning Architectures. Machine Learning and Knowledge Extraction, 6(3), 1545-1563.

Lin, Y. (2024). Application and Challenges of Computer Networks in Distance Education. Computing, Performance and Communication Systems, 8(1), 17-24.

Lin, Y. (2024). Design of urban road fault detection system based on artificial neural network and deep learning. Frontiers in neuroscience, 18, 1369832.

Hagström, L., & Johansson, R. (2022). How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input?. arXiv preprint arXiv:2209.08982.

Chen, T., Lian, J., & Sun, B. (2024). An Exploration of the Development of Computerized Data Mining Techniques and Their Application. International Journal of Computer Science and Information Technology, 3(1), 206-212.

Zhang, Y., Yang, K., Wang, Y., Yang, P., & Liu, X. (2023, July). Speculative ECC and LCIM Enabled NUMA Device Core. In 2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) (pp. 624-631). IEEE.

Xia, Y., Liu, S., Yu, Q., Deng, L., Zhang, Y., Su, H., & Zheng, K. (2023). Parameterized Decision-making with Multi-modal Perception for Autonomous Driving. arXiv preprint arXiv:2312.11935.

Khalil, M. M. Y., Wang, Q., Chen, B., & Wang, W. (2023). Cross-modality representation learning from transformer for hashtag prediction. Journal of Big Data, 10(1), 148.

Liu, M., & Li, Y. (2023, October). Numerical analysis and calculation of urban landscape spatial pattern. In 2nd International Conference on Intelligent Design and Innovative Technology (ICIDIT 2023) (pp. 113-119). Atlantis Press.

Tu, H., Shi, Y., & Xu, M. (2023, May). Integrating conditional shape embedding with generative adversarial network-to assess raster format architectural sketch. In 2023 Annual Modeling and Simulation Conference (ANNSIM) (pp. 560-571). IEEE.

Kaur, P., Pannu, H. S., & Malhi, A. K. (2021). Comparative analysis on cross-modal information retrieval: A review. Computer Science Review, 39, 100336.

Yang, Y., Jiménez-Negrón, O. A., & Kitchin, J. R. (2021). Machine-learning accelerated geometry optimization in molecular simulation. The Journal of Chemical Physics, 154(23).

Hadi, M. U., Qureshi, R., Shah, A., Irfan, M., Zafar, A., Shaikh, M. B., ... & Mirjalili, S. (2023). A survey on large language models: Applications, challenges, limitations, and practical usage. Authorea Preprints.

Lin, Y. (2023). Optimization and Use of Cloud Computing in Big Data Science. Computing, Performance and Communication Systems, 7(1), 119-124.

Lin, Y. Discussion on the Development of Artificial Intelligence by Computer Information Technology.

Koh, J. Y., Fried, D., & Salakhutdinov, R. R. (2024). Generating images with multimodal language models. Advances in Neural Information Processing Systems, 36.

Yang, J. (2024). Data-Driven Investment Strategies in International Real Estate Markets: A Predictive Analytics Approach. International Journal of Computer Science and Information Technology, 3(1), 247-258.

Yang, J. (2024). Comparative Analysis of the Impact of Advanced Information Technologies on the International Real Estate Market. Transactions on Economics, Business and Management Research, 7, 102-108.

Yang, J. (2024). Application of Business Information Management in Cross-border Real Estate Project Management. International Journal of Social Sciences and Public Administration, 3(2), 204-213.

Salemi, A., & Zamani, H. (2024, July). Evaluating Retrieval Quality in Retrieval-Augmented Generation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2395-2400).

Qiu, L., & Liu, M. (2024). Innovative Design of Cultural Souvenirs Based on Deep Learning and CAD.

Zhang, Y., Zhang, C., Tang, Y., & He, Z. (2024). Cross-modal concept learning and inference for vision-language models. Neurocomputing, 583, 127530.

Shi, Y., Ma, C., Wang, C., Wu, T., & Jiang, X. (2024, May). Harmonizing Emotions: An AI-Driven Sound Therapy System Design for Enhancing Mental Health of Older Adults. In International Conference on Human-Computer Interaction (pp. 439-455). Cham: Springer Nature Switzerland.

Chen, Y., & Bazzani, L. (2020). Learning joint visual semantic matching embeddings for language-guided retrieval. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXII 16 (pp. 136-152). Springer International Publishing.

Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in neural information processing systems, 32.

Wang, J., Zhang, H., Zhong, Y., Liang, Y., Ji, R., & Cang, Y. (2024). Advanced Multimodal Deep Learning Architecture for Image-Text Matching. arXiv preprint arXiv:2406.15306.

Wang, J., Li, X., Jin, Y., Zhong, Y., Zhang, K., & Zhou, C. (2024). Research on image recognition technology based on multimodal deep learning. arXiv preprint arXiv:2405.03091.

Koh, J. Y., Fried, D., & Salakhutdinov, R. R. (2024). Generating images with multimodal language models. Advances in Neural Information Processing Systems, 36.

Soana, V., Shi, Y., & Lin, T. A Mobile, Shape-Changing Architectural System: Robotically-Actuated Bending-Active Tensile Hybrid Modules.

Zhong, Y., Liu, Y., Gao, E., Wei, C., Wang, Z., & Yan, C. (2024). Deep Learning Solutions for Pneumonia Detection: Performance Comparison of Custom and Transfer Learning Models. medRxiv, 2024-06.

Wang, C., Yang, H., Chen, Y., Sun, L., Zhou, Y., & Wang, H. (2010). Identification of Image-spam Based on SIFT Image Matching Algorithm. JOURNAL OF INFORMATION &COMPUTATIONAL SCIENCE, 7(14), 3153-3160.

Wang, C., Yang, H., Chen, Y., Sun, L., Wang, H., & Zhou, Y. (2012). Identification of Image-spam Based on Perimetric Complexity Analysis and SIFT Image Matching Algorithm. JOURNAL OF INFORMATION &COMPUTATIONAL SCIENCE, 9(4), 1073-1081.

Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., & Liu, J. (2022). Human action recognition from various data modalities: A review. IEEE transactions on pattern analysis and machine intelligence, 45(3), 3200-3225.

An, L., Song, C., Zhang, Q., & Wei, X. (2024). Methods for assessing spillover effects between concurrent green initiatives. MethodsX, 12, 102672.

Shih, H. C., Wei, X., An, L., Weeks, J., & Stow, D. (2024). Urban and Rural BMI Trajectories in Southeastern Ghana: A Space-Time Modeling Perspective on Spatial Autocorrelation. International Journal of Geospatial and Environmental Research, 11(1), 3.

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Published

29-07-2024

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Articles

How to Cite

Zhou, R. (2024). Advanced Embedding Techniques in Multimodal Retrieval Augmented Generation A Comprehensive Study on Cross Modal AI Applications. Journal of Computing and Electronic Information Management, 13(3), 16-22. https://doi.org/10.54097/h8wf8vah