International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Multimodal Large Language Models: Architectures, Challenges, Techniques and Future Directions |
|---|---|
| ABSTRACT | This review presents a detailed examination of the development, structural design, and deployment of Multimodal Large Language Models (LLMs), emphasizing their growing influence across a wide range of application areas. The paper discusses the evolution from conventional text-centric language models to advanced multimodal architectures capable of jointly interpreting multiple data formats. Core architectural paradigms—such as Cross-Modality Attention mechanisms and Unified Embedding Decoder models—are analyzed with respect to their advantages, limitations, and practical implications. Additionally, the study explores implementation components, technical constraints, and real-world use cases while addressing critical developmental factors, including dataset construction, computational scalability, and quality validation processes. By evaluating integration strategies, assessment methodologies, and emerging innovations, this work outlines the current capabilities and future trajectory of multimodal AI systems. These advancements represent an important step toward creating adaptive and intuitive AI technologies that more closely resemble human cognitive behavior, while also highlighting the challenges and opportunities that accompany this rapid progress. |
| AUTHOR | AVINASH ALUGOLU, DR.PRASADU PEDDI, DR.C.RAMASESHAGIRI RAO Research Scholar, Department of CSE, Sikkim Alpine University, Sikkim, India Professor, Department of CSE, Sikkim Alpine University, Sikkim, India Professor, Department of CSE, Pallavi Engineering College, Hyderabad, India |
| VOLUME | 154 |
| DOI | DOI: 10.15680/IJIRCCE.2024.1202203 |
| pdf/203_Multimodal Large Language Models Architectures, Challenges, Techniques and Future Directions.pdf | |
| KEYWORDS | |
| References | [1] AshishVaswani et al., "Attention Is All You Need," in Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017, pp. 5998- 6008. Attention Is All You Need - Wikipedia [2] JiasenLuetal.,"ViLBERT:PretrainingTask-AgnosticVisiolinguisticRepresentations for Vision-and-Language Tasks," in Advances in Neural Information Processing Systems, 2019, pp. 13-23. 2019. https://arxiv.org/abs/1908.02265 [3] David Wilson, "Vision-Language Models: Unlocking the Future of Multimodal AI," 2024. https://www.autonomous.ai/ourblog/vision-language-models [4] Shukang Yin et al., "A Survey on Multimodal Large Language Models," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 19, no. 6, pp. 2595- 2608, 2024. https://arxiv.org/abs/2306.13549 [5] AnurajSinghandHarithaNair,"ANeuralArchitectureSearchforAutomated MultimodalLearning,"2022.https://www.sciencedirect.com/science/article/abs/pii/S0957417422012581 [6] KonstantinosPoulinakis,"MultimodalDeepLearning:Definition,Examples,Applications,"2022.https://www.v7labs.com/blog/multimodal-deep-learning-guide [7] Nikhil Patel, "Challenges and Solutions in Implementing Multimodal Learning Remotely," 2023. https://www.slideshare.net/slideshow/challenges-and-solutions-in-implementing-multimodal-learning-remotely/260983907 [8] Galileo,"Multimodal AI: Evaluation Strategies for Technical Teams, 2025. https://www.galileo.ai/blog/multimodal-ai-guide [9] SudiptoDatta, RanjitBarua and Jonali Das, "Application of Artificial Intelligence in Modern Healthcare System," IntechOpen, ch. 5, pp. 78-95, 2019. https://www.intechopen.com/chapters/70446 |
