Large Language Models and Multimodal AI

Abstract

Large Language Models (LLMs) have transformed natural language processing by demonstrating remarkable capabilities in text understanding, generation, and reasoning. Recent advances extend these models into multimodal AI, enabling the integration of multiple data modalities—such as text, images, audio, and video—into unified learning frameworks. Multimodal AI systems leverage LLMs to process and correlate information across modalities, enhancing context understanding, task flexibility, and human–computer interaction. These models find applications in image captioning, visual question answering, video summarization, conversational AI, and cross-modal retrieval. Despite their promise, challenges such as high computational requirements, alignment of heterogeneous modalities, interpretability, and ethical concerns remain. This paper explores the architecture, capabilities, applications, and limitations of LLMs and multimodal AI, highlighting their potential to enable more robust, context-aware, and interactive artificial intelligence systems.