Understanding the Emergence of Multimodal Representation Alignment

Megan Tjandrasuwita, Chanakya Ekbote, Liu Ziyin, Paul Pu Liang
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:59723-59760, 2025.

Abstract

Multimodal representation learning is fundamentally about transforming incomparable modalities into comparable representations. While prior research has primarily focused on explicitly aligning these representations through targeted learning objectives and model architectures, a recent line of work has found that independently trained unimodal models of increasing scale and performance can become implicitly aligned with each other. These findings raise fundamental questions regarding the emergence of aligned representations in multimodal learning. Specifically: (1) when and why does alignment emerge implicitly? and (2) is alignment a reliable indicator of performance? Through a comprehensive empirical investigation, we demonstrate that both the emergence of alignment and its relationship with task performance depend on several critical data characteristics. These include, but are not necessarily limited to, the degree of similarity between the modalities and the balance between redundant and unique information they provide for the task. Our findings suggest that alignment may not be universally beneficial; rather, its impact on performance varies depending on the dataset and task. These insights can help practitioners determine whether increasing alignment between modalities is advantageous or, in some cases, detrimental to achieving optimal performance.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-tjandrasuwita25a, title = {Understanding the Emergence of Multimodal Representation Alignment}, author = {Tjandrasuwita, Megan and Ekbote, Chanakya and Ziyin, Liu and Liang, Paul Pu}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {59723--59760}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/tjandrasuwita25a/tjandrasuwita25a.pdf}, url = {https://proceedings.mlr.press/v267/tjandrasuwita25a.html}, abstract = {Multimodal representation learning is fundamentally about transforming incomparable modalities into comparable representations. While prior research has primarily focused on explicitly aligning these representations through targeted learning objectives and model architectures, a recent line of work has found that independently trained unimodal models of increasing scale and performance can become implicitly aligned with each other. These findings raise fundamental questions regarding the emergence of aligned representations in multimodal learning. Specifically: (1) when and why does alignment emerge implicitly? and (2) is alignment a reliable indicator of performance? Through a comprehensive empirical investigation, we demonstrate that both the emergence of alignment and its relationship with task performance depend on several critical data characteristics. These include, but are not necessarily limited to, the degree of similarity between the modalities and the balance between redundant and unique information they provide for the task. Our findings suggest that alignment may not be universally beneficial; rather, its impact on performance varies depending on the dataset and task. These insights can help practitioners determine whether increasing alignment between modalities is advantageous or, in some cases, detrimental to achieving optimal performance.} }
Endnote
%0 Conference Paper %T Understanding the Emergence of Multimodal Representation Alignment %A Megan Tjandrasuwita %A Chanakya Ekbote %A Liu Ziyin %A Paul Pu Liang %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-tjandrasuwita25a %I PMLR %P 59723--59760 %U https://proceedings.mlr.press/v267/tjandrasuwita25a.html %V 267 %X Multimodal representation learning is fundamentally about transforming incomparable modalities into comparable representations. While prior research has primarily focused on explicitly aligning these representations through targeted learning objectives and model architectures, a recent line of work has found that independently trained unimodal models of increasing scale and performance can become implicitly aligned with each other. These findings raise fundamental questions regarding the emergence of aligned representations in multimodal learning. Specifically: (1) when and why does alignment emerge implicitly? and (2) is alignment a reliable indicator of performance? Through a comprehensive empirical investigation, we demonstrate that both the emergence of alignment and its relationship with task performance depend on several critical data characteristics. These include, but are not necessarily limited to, the degree of similarity between the modalities and the balance between redundant and unique information they provide for the task. Our findings suggest that alignment may not be universally beneficial; rather, its impact on performance varies depending on the dataset and task. These insights can help practitioners determine whether increasing alignment between modalities is advantageous or, in some cases, detrimental to achieving optimal performance.
APA
Tjandrasuwita, M., Ekbote, C., Ziyin, L. & Liang, P.P.. (2025). Understanding the Emergence of Multimodal Representation Alignment. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:59723-59760 Available from https://proceedings.mlr.press/v267/tjandrasuwita25a.html.

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