MAF: Multimodal Adaptive Few-shot Prompting for Sentiment Analysis with MLLMs

The article introduces the Multimodal Adaptive Few-Shot Prompting (MAF) framework for enhancing sentiment analysis using multimodal large language models (MLLMs). MAF employs a demonstration retrieval module that integrates facial expressions, scene context, and textual semantics, utilizing a lightweight coefficient generation network for real-time query-conditioned fusion of multimodal data. This approach significantly improves prediction stability and performance on benchmark datasets, making it a valuable technique for practitioners aiming to optimize sentiment analysis in complex multimodal environments.