arXivAI Research
Overcoming Incomplete Data in MSA: SemMSA Harnesses LLM Latent Semantics and Spectral Alignment
突破多模態情緒分析瓶頸：SemMSA 藉由 LLM 潛在語意與無錨點頻譜對齊解決資料缺失問題
The proposed SemMSA framework leverages frozen LLMs to extract latent semantics and applies anchor-free spectral alignment to address incomplete modality data in sentiment analysis.
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