@inproceedings{yin-etal-2025-sea,
title = "{SEA}: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in {MLLM}s",
author = "Yin, Yuanyang and
Zhao, Yaqi and
Zhang, Yajie and
Zhang, Yuanxing and
Lin, Ke and
Wang, Jiahao and
Tao, Xin and
Wan, Pengfei and
Zhang, Wentao and
Zhao, Feng",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.55/",
pages = "1058--1070",
ISBN = "979-8-89176-332-6",
abstract = "Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities by integrating visual and textual inputs, yet modality alignment remains one of the most challenging aspects. Current MLLMs typically rely on simple adapter architectures and pretraining approaches to bridge vision encoders with large language models (LLM), guided by image-level supervision. We identify this paradigm often leads to suboptimal alignment between modalities, significantly constraining the LLM{'}s ability to properly interpret and reason with visual features particularly for smaller language models. To address this fundamental limitation, we propose Supervised Embedding Alignment (SEA), a token-level supervision alignment method that enables more precise visual-text alignment during pretraining. SEA introduces minimal computational overhead while preserving language capabilities and substantially improving cross-modal understanding. Our comprehensive analyses reveal critical insights into the adapter{'}s role in multimodal integration, and extensive experiments demonstrate that SEA consistently improves performance across various model sizes, with smaller models benefiting the most (average performance gain of 7.61{\%} for Gemma-2B). This work establishes a foundation for developing more effective alignment strategies for future multimodal systems."
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<abstract>Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities by integrating visual and textual inputs, yet modality alignment remains one of the most challenging aspects. Current MLLMs typically rely on simple adapter architectures and pretraining approaches to bridge vision encoders with large language models (LLM), guided by image-level supervision. We identify this paradigm often leads to suboptimal alignment between modalities, significantly constraining the LLM’s ability to properly interpret and reason with visual features particularly for smaller language models. To address this fundamental limitation, we propose Supervised Embedding Alignment (SEA), a token-level supervision alignment method that enables more precise visual-text alignment during pretraining. SEA introduces minimal computational overhead while preserving language capabilities and substantially improving cross-modal understanding. Our comprehensive analyses reveal critical insights into the adapter’s role in multimodal integration, and extensive experiments demonstrate that SEA consistently improves performance across various model sizes, with smaller models benefiting the most (average performance gain of 7.61% for Gemma-2B). This work establishes a foundation for developing more effective alignment strategies for future multimodal systems.</abstract>
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%0 Conference Proceedings
%T SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs
%A Yin, Yuanyang
%A Zhao, Yaqi
%A Zhang, Yajie
%A Zhang, Yuanxing
%A Lin, Ke
%A Wang, Jiahao
%A Tao, Xin
%A Wan, Pengfei
%A Zhang, Wentao
%A Zhao, Feng
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F yin-etal-2025-sea
%X Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities by integrating visual and textual inputs, yet modality alignment remains one of the most challenging aspects. Current MLLMs typically rely on simple adapter architectures and pretraining approaches to bridge vision encoders with large language models (LLM), guided by image-level supervision. We identify this paradigm often leads to suboptimal alignment between modalities, significantly constraining the LLM’s ability to properly interpret and reason with visual features particularly for smaller language models. To address this fundamental limitation, we propose Supervised Embedding Alignment (SEA), a token-level supervision alignment method that enables more precise visual-text alignment during pretraining. SEA introduces minimal computational overhead while preserving language capabilities and substantially improving cross-modal understanding. Our comprehensive analyses reveal critical insights into the adapter’s role in multimodal integration, and extensive experiments demonstrate that SEA consistently improves performance across various model sizes, with smaller models benefiting the most (average performance gain of 7.61% for Gemma-2B). This work establishes a foundation for developing more effective alignment strategies for future multimodal systems.
%U https://aclanthology.org/2025.emnlp-main.55/
%P 1058-1070
Markdown (Informal)
[SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs](https://aclanthology.org/2025.emnlp-main.55/) (Yin et al., EMNLP 2025)
ACL
- Yuanyang Yin, Yaqi Zhao, Yajie Zhang, Yuanxing Zhang, Ke Lin, Jiahao Wang, Xin Tao, Pengfei Wan, Wentao Zhang, and Feng Zhao. 2025. SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 1058–1070, Suzhou, China. Association for Computational Linguistics.