LAPM-RA: Reward-Adaptive Prompt Learning with LLM Augmentation for Multimodal Sentiment Analysis

Abstract

Few-shot multimodal sentiment analysis (MSA), constrained by limited annotated data, often suffers from large cross-modal semantic alignment gaps and difficulty in capturing fine-grained sentiment cues, making it challenging to fully exploit the complementary information between text and images. Recent advances in large language models (LLMs) and prompt learning have shown strong potential for improving label efficiency in low-resource natural language processing tasks; however, their direct application to MSA is hindered by static prompt designs and shallow cross-modal integration. To overcome these limitations, we propose LLM-Augmented Prompt Learning for Multimodal Sentiment Analysis with Reward Adaptation (LAPM-RA), a unified framework integrating LLM-based sentiment-aware augmentation, reward-guided prompt selection, and context-aware multimodal fusion. Specifically, LLMs generates sentiment-consistent and counterfactual text variants to enhance lexical and structural diversity while preserving label fidelity; a supervised policy network adaptively selects optimal prompt templates based on reward signals; and a lightweight gating mechanism integrates textual and visual embeddings contextually. Extensive experiments on multiple benchmarks validate the effectiveness and robustness of LAPM-RA over competitive baselines.