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

2026-06-14 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

The MAF (Multimodal Adaptive Few-Shot Prompting) framework addresses the acute prompt sensitivity of Multimodal Large Language Models (MLLMs) in sentiment analysis. MAF dynamically retrieves and integrates query-relevant demonstrations to improve MLLM sentiment reasoning by adapting to varying multimodal cues. It features a demonstration retrieval module that encodes facial expressions, scene context, and textual semantics, incorporating a lip movement amplitude detection mechanism for accurate speaker identification in multi-person scenarios. A lightweight coefficient generation network outputs real-time, query-conditioned fusion weights for weighted aggregation of multimodal similarity scores, retrieving the top-K informative demonstrations. Prediction stability is further enhanced through majority voting, leading to substantial and consistent performance improvements on public benchmark datasets.

Key takeaway

For Machine Learning Engineers developing MLLM-based sentiment analysis systems, MAF offers a robust approach to overcome prompt design limitations. You should consider integrating adaptive few-shot prompting, especially when dealing with nuanced multimodal inputs or multi-person scenarios, to dynamically retrieve relevant demonstrations and enhance prediction stability. This method can significantly improve your model's performance and context-sensitivity.

Key insights

MAF dynamically adapts few-shot prompts for MLLMs in sentiment analysis by integrating multimodal cues and adaptive demonstration retrieval.

Principles

• Dynamic prompt adaptation improves MLLM performance.
• Multimodal cues require adaptive fusion.
• Majority voting enhances prediction stability.

Method

MAF constructs a demonstration retrieval module encoding facial, scene, text, and lip movement data, trains a coefficient generation network for real-time fusion weights, aggregates multimodal similarity scores, retrieves top-K demonstrations, and applies majority voting.

In practice

• Improve MLLM sentiment analysis accuracy.
• Handle multi-person video sentiment tasks.
• Reduce MLLM prompt sensitivity.

Topics

• Multimodal LLMs
• Sentiment Analysis
• Few-shot Prompting
• Adaptive Prompting
• Multimodal Fusion
• Demonstration Retrieval

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.