Papers by Yuanyang Yin

1 papers
SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs (2025.emnlp-main)

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Challenge: Multimodal Large Language Models (MLLMs) integrate visual and textual inputs, yet modality alignment remains one of the most challenging aspects.
Approach: They propose a token-level supervision alignment method that enables more precise visual-text alignment during pretraining.
Outcome: The proposed method improves performance across various model sizes, with smaller models benefiting the most.

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