Sentiment analysis from multimodal data — images, video, and text — is increasingly important for enterprises monitoring customer feedback, brand perception, and employee sentiment. However, multimodal large language models (MLLMs) exhibit acute sensitivity to prompt design, according to a new research paper by Hangling Xie posted on arXiv. Static, uniformly applied prompts are inherently suboptimal for capturing the nuanced multimodal cues that vary across inputs. To address this limitation, the paper proposes a Multimodal Adaptive Few-Shot Prompting (MAF) framework, which dynamically retrieves and integrates query-relevant demonstrations to elicit the sentiment reasoning capabilities of MLLMs in a context-sensitive manner.
How MAF Works
The MAF framework constructs a demonstration retrieval module that holistically encodes three modalities: facial expressions, scene context, and textual semantics. A key innovation is a lip movement amplitude detection mechanism introduced for accurate speaker identification in multi-person scenarios. Unlike conventional fixed-weight fusion, MAF uses a lightweight coefficient generation network that is trained to output query-conditioned fusion weights in real time. This enables weighted aggregation of multimodal similarity scores to retrieve the top-K most informative demonstrations for each input.
To further enhance prediction stability, the framework employs majority voting over multiple candidate outputs generated by the MLLM. This reduces variance and improves reliability.
Performance on Benchmarks
Extensive experiments on public benchmark datasets demonstrate that MAF achieves substantial and consistent performance improvements over the corresponding backbone variants, according to the paper. It also remains competitive with strong multimodal sentiment-analysis baselines. The specific datasets and exact accuracy gains are not detailed in the abstract, but the results indicate robust gains across different MLLM backbones.
Enterprise Relevance
While the paper is primarily a research contribution, the underlying technique has clear implications for enterprise applications that rely on accurate sentiment extraction from multimodal customer interactions, such as video call analytics, social media monitoring, and product review analysis. The ability to dynamically adapt prompts based on input content could reduce the need for manual prompt engineering, saving time and improving consistency.
| Feature | Traditional Static Prompting | MAF Dynamic Prompting |
|---|---|---|
| Demonstration selection | Fixed set per task | Query-relevant retrieval |
| Modality fusion | Fixed weights | Learned, query-conditioned weights |
| Speaker identification | None | Lip movement detection |
| Stability technique | Single output | Majority voting |
Technical Stack and Validation
The MAF framework is designed to work with any MLLM backbone. The demonstration retrieval module encodes facial expressions, scene context, and textual semantics. The lightweight coefficient generation network is trained separately from the MLLM, allowing efficient inference. The lip movement detection mechanism adds a new dimension to speaker identification in multi-person scenarios, addressing a common challenge in group video analysis.
The paper does not specify the exact architecture or training data for the coefficient network, but notes that it outputs fusion weights in real time. Validation is performed on public benchmark datasets, and the results show improvements over both the backbone models and existing multimodal sentiment analysis baselines.
The MAF framework represents a step toward more adaptive and context-aware sentiment analysis using MLLMs, potentially reducing the manual effort required to craft effective prompts and improving accuracy across diverse multimodal inputs.