Representation Potentials of Foundation Models for Multimodal Alignment: A Survey (2510.05184v1)

Abstract: Foundation models learn highly transferable representations through large-scale pretraining on diverse data. An increasing body of research indicates that these representations exhibit a remarkable degree of similarity across architectures and modalities. In this survey, we investigate the representation potentials of foundation models, defined as the latent capacity of their learned representations to capture task-specific information within a single modality while also providing a transferable basis for alignment and unification across modalities. We begin by reviewing representative foundation models and the key metrics that make alignment measurable. We then synthesize empirical evidence of representation potentials from studies in vision, language, speech, multimodality, and neuroscience. The evidence suggests that foundation models often exhibit structural regularities and semantic consistencies in their representation spaces, positioning them as strong candidates for cross-modal transfer and alignment. We further analyze the key factors that foster representation potentials, discuss open questions, and highlight potential challenges.

• The paper presents a comprehensive analysis of foundation models that unify multimodal data across vision, language, and speech through extensive pretraining.
• It details methodological approaches using metrics like CKA, CCA, and MNN to quantify representation similarity and cross-modal transferability.
• The study highlights how model scale, architecture, and training paradigms drive the emergence of shared representations, even revealing parallels with neuroscientific patterns.

Representation Potentials of Foundation Models for Multimodal Alignment: A Survey

Introduction

The paper "Representation Potentials of Foundation Models for Multimodal Alignment: A Survey" presents an exhaustive analysis of the capabilities of foundation models in representing and unifying multimodal data. Foundation models, characterized by their ability to learn highly transferable representations through extensive pretraining on diverse datasets, have shown the potential to generalize across different architectures and modalities. This survey investigates the latent capacity of these representations to encapsulate specific task information within a modality while providing a basis for inter-modal alignment and unification. The paper covers various manifestations of this potential across vision, language, speech, multimodal tasks, and even neuroscientific contexts, emphasizing the structural regularities and semantic consistencies within their representation spaces.

Foundation Models Across Modalities

Foundation models span several domains, significantly impacting NLP, computer vision, and speech processing. In computer vision, models such as Vision Transforme (ViT) and ResNet are trained on large-scale datasets, achieving robust transferability across visual tasks. In NLP, LLMs like BERT and GPT are trained to capture syntactic and semantic structures, facilitating their application across diverse language tasks. Similarly, in speech processing, models like wav2vec and HuBERT are designed to learn representations directly from audio data. The survey highlights how these models function as the backbone for foundational tasks across these domains, driven by self-supervised learning and architectural advances.

Metrics for Representation Alignment

The survey reviews key metrics that render representation alignment measurable, introducing Centered Kernel Alignment (CKA), Canonical Correlation Analysis (CCA), and Mutual Nearest Neighbors (MNN) as primary tools for quantifying the degree of similarity between learned representations. These metrics help determine whether different models or modalities encode similar information, providing a basis for assessing alignment and transferability across different neural networks.

Evidence for Representation Potentials

Across modalities, foundation models exhibit significant alignment in their learned representations:

1. Vision and Language: Studies reveal that vision models like CNNs and ViTs naturally align in internal representation, even when trained with different objectives or datasets. LLMs similarly demonstrate alignment in linguistic abstraction and transferable feature spaces.
2. Speech Models: Speech foundation models exhibit hierarchical representation similarity, reflecting their ability to capture consistent informational structures across architectural variations.
3. Cross-Modal Alignment: The survey notes that models trained independently on different modalities often converge towards shared structural representations, suggesting inherent alignment potential. This is evident in vision-language alignments and auditory-language correspondence detected through learned representations.
4. Neuroscience Correlation: Foundation models capture representations analogous to brain activity patterns observed in neuroscientific studies, showcasing potential correspondence between artificial and biological cognition.

Factors Driving Alignment Potential

Several factors influence the alignment potential of foundation models, including model scale, architectural choices, and the diversity of training objectives:

• Scale: Larger foundation models, trained on diverse and extensive datasets, tend to develop more aligned representations, pointing to a convergence of learned abstractions as training resources increase.
• Architectural Design: The inductive biases inherent in transformers and other architectural configurations facilitate generalizable and transferable representation learning across varied tasks and domains.
• Training Paradigms: Self-supervised learning encourages models to develop representations that generalize well, supporting task-agnostic abstraction and alignment across models.

Open Questions and Future Directions

Despite the evidence for alignment, challenges remain in defining rigorous evaluation frameworks for representation similarity, addressing the influence of data biases and sociotechnical context, and understanding the full extent of alignment in specialized or narrowly focused models. Furthermore, the representation alignment across all modalities and tasks is not uniform, with inherent differences in sensor modalities potentially limiting complete convergence.

Conclusion

The survey underscores the representation potential of foundation models for multimodal alignment, emphasizing the empirical evidence across vision, language, speech, and biological contexts. By analyzing factors that foster this potential and outlining open questions, the paper provides a foundational understanding of how these models can align and unify multimodal tasks, informing future research to harness their full capabilities in advancing AI integration and generalist applications.