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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

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Overcoming Incomplete Data in MSA: SemMSA Harnesses LLM Latent Semantics and Spectral Alignment
The 30-second version

Multimodal Sentiment Analysis (MSA) often suffers from incomplete raw inputs, where conventional imputation methods suffer from spurious generation. SemMSA resolves this through Cross-modal Semantic Refinement (CSR) and Cross-modal Spectral Alignment (CSA). CSR adaptively projects visual and acoustic representations into a frozen LLM space to extract continuous latent semantics without explicit decoding. CSA then captures global nonlinear dependencies by enhancing the dominant spectral component of their kernel Gram matrix, bypassing predefined anchor modalities. SemMSA achieves state-of-the-art results on SIMS, MOSI, and MOSEI benchmarks.

Key points

01

Latent Semantic Refinement

The CSR module maps visual and acoustic features into a frozen LLM embedding space, iteratively refining continuous semantic states without expensive explicit decoding.

02

Anchor-Free Spectral Alignment

CSA aligns all representations globally by enhancing the dominant spectral component of their kernel Gram matrix, avoiding reliance on a pre-defined anchor modality.

03

Anti-Collapse Spectral Separation

An instance-level spectral separation constraint is integrated to maintain cross-sample discriminability and prevent representation collapse.

How it works

SemMSA Dual-Core Pipeline Flow
Via adaptersToken-efficient loopAlign componentsPredict sentimentIncomplete MultimodalInputCSR: LLM Space MappingRefined LatentSemanticsCSA: Spectral AlignmentRobust SentimentPrediction

Why it matters

In real-world settings, multimodal signals like audio or video are frequently missing. SemMSA demonstrates that instead of hallucinating raw features, systems can leverage frozen LLMs as high-level semantic anchors for robust feature alignment. This provides a lightweight and noise-resistant architecture for multimodal AI agents to understand human emotions under partial sensory inputs.

Who it affects

  • AI Researcher
  • AI Developer
  • Product Manager

How to use it

  1. 1Multimodal sentiment analysis in wild environments where video or audio tracks are partially corrupted or missing.
  2. 2Integration into empathetic conversational agents to sustain reliable human-robot interaction under incomplete sensor inputs.

Limitations & caveats

  • The performance relies on the frozen LLM embedding space, which might be limited by the pre-trained LLM's own semantic capacity.
  • Under extreme scenarios where almost all primary modalities are missing, the limits of the semantic refinement require further validation.

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