Multimodal sentiment analysis is a trending topic with the explosion of
multimodal content on the web. Present studies in multimodal sentiment analysis
rely on large-scale supervised data. Collating supervised data is
time-consuming and labor-intensive. As such, it is essential to investigate the
problem of few-shot multimodal sentiment analysis. Previous works in few-shot
models generally use language model prompts, which can improve performance in
low-resource settings. However, the textual prompt ignores the information from
other modalities. We propose Multimodal Probabilistic Fusion Prompts, which can
provide diverse cues for multimodal sentiment detection. We first design a
unified multimodal prompt to reduce the discrepancy in different modal prompts.
To improve the robustness of our model, we then leverage multiple diverse
prompts for each input and propose a probabilistic method to fuse the output
predictions. Extensive experiments conducted on three datasets confirm the
effectiveness of our approach.

本研究提出多模态概率融合提示方法，结合语言模型提示和多模态提示提供不同的提示信号，以提高少样本情感分析中的准确性。实验结果表明该方法在三个数据集上均有效。