Applications and future directions in multimodal large language model: opportunities and challenges
automation; customer care; healthcare assistant; information management; machine learning; Multimodal large language models
Multimodal large language models (MLLMs) are an application of artificial intelligence that is rapidly growing by integrating numerous use cases. MLLMs have the capability to process data from several sources, including structured and unstructured. It enables large language models (LLMs) to give insights to the user by analyzing data from various formats. The traditional way of analyzing the data was done with a single data format. While using MLLMs, multiple data modalities are handled to manage complicated multimodal tasks, like generating content, multimodal perception, and augmenting human-computer interaction. This chapter discusses in detail the insights from data from multiple modalities and domains of the use cases of MLLMs. We also discuss the advantages of MLLMs and explain the transformative benefits from unimodal systems to multimodal systems in different sectors. We also focus on the ethical usage of MLLMs by addressing the challenges related to privacy, operational limits, bias, computational difficulties, and data scarcity. The scarce assessment metrics and trials in accomplishing robust explainability are also discussed. To train these MLLMs, acquiring and training the data utilizes more computation and power consumption, along with addressing the data security and privacy concerns. The chapter also discusses the sensible usage of AI through different problems and practices. Detailed analysis and strategies for addressing global challenges and promoting novelty in model development, outlining how these MLLMs shape the upcoming technological innovation focusing on ethical application of technology with an advantage on society. 2026 Elsevier Inc. All rights reserved.
Chandra, J.; Malviya, Meenakshi; Sabu, Samson; Rajendran, Rajesh Kanna; Joseph, Alwin
Challenges and Applications of Generative Large Language Models;pp.219-241
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1016/B978-0-443-33592-1.00006-1 https://www.scopus.com/pages/publications/105032983757?origin=resultslist
Chandra J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Malviya M., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Sabu S., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Rajendran R.K., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Joseph A., Department of Computer Science, Christ University, Karnataka, Bengaluru, India