Preprint
Jul 2026
Semantic-Aligned Structural Abstraction for Multimodal Sentiment Analysis
SentiLLM is proposed, a unified framework that leverages Semantic-Aligned Structural Abstraction to distill continuous raw signals into compact, semantically meaningful tokens and significantly improves discriminative performance with only a small number of trainable parameters.
Wei Chen, Junkai Li, Tongguan Wang et al.
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