Cross-Modal Alignment in Multimodal Large Language Models: Mechanisms, Challenges, and Future Directions

Abstract

As a significant breakthrough in recent years for artificial intelligence research, multimodal large-scale language models have extended the ability of language models across multiple dimensions, including vision and hearing, into non-linguistic forms through technical processes such as cross-modal alignment. Examine the mechanisms, problems and future paths for cross-modal alignment in multimodal large-scale language models based on representations learned by using the theory of representation learning, transformer-based attention mechanism and information-theoretical framework as core references. Three interrelated arguments are put forward: Cross-modal alignment is no longer a one-shot technical task but rather comprises three distinct paths: contrastive learning, cross-modal attention fusion, and instruction-based fine-tuning; The main problems faced by the research on cross-modal alignment, such as modality gap, hallucination, data bias and evaluation difficulties, all stem from their intrinsic diversity among various representation modes. Therefore, some promising future directions should be explored, which includes precise semantic alignment, extension to videos or soundscapes, developing transparent interpretation systems to help people better understand the system, etc. This paper presents an empirical integration of the rapid evolution of a highly dynamic technological system for researchers studying multimodal alignment in related disciplines.