Investigating the Efficacy of Multimodal Large Language Models in Cross-Domain Knowledge Transfer
Mohammed Karimkhan Pathan · Premier Journal of Artificial Intelligence · 2025
Multimodal large language models (MLLMs) have emerged as powerful tools for a diverse range of applications, particularly in enabling effective cross-domain knowledge transfer. By leveraging multimodal embeddings and transfer learning, MLLMs process and understand information from text, images, videos, and audio, enabling their capacity to generalize across various domains’ content without requiring domain-specific training. This research investigates the efficacy of MLLMs in transferring knowledge across different domains, focusing on their underlying mechanisms that facilitate generalization, including the capture of semantic relationships and patterns. We examine factors influencing the effectiveness of cross-domain knowledge transfer, such as the similarity between source and target domains, the quality and quantity of training data, and the architecture of the MLLM. Through empirical studies and case analyses, we demonstrate the potential of MLLMs to revolutionize various fields, including healthcare, education, and engineering. Experimental results highlight the capacity of MLLMs to improve context comprehension and reduce computational overhead, suggesting a scalable and adaptable future for AI systems poised to drive innovation and transformation across diverse industries.