arXiv:2606.15694v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in understanding complex multimodal content. However, their performance in sentiment analysis exhibits acute sensitivity to prompt design, rendering static, uniformly applied prompts inherently suboptimal for capturing the nuanced multimodal cues that vary across inputs. To address this limitation, we propose a Multimodal Adaptive Few-Shot Prompting (MAF) framework, which dynamically retrieves and integrates query-relevant demonstrations to elicit the sentiment r
The proliferation of Multimodal Large Language Models (MLLMs) and their application to complex tasks like sentiment analysis demands more sophisticated prompting strategies to maximize their utility.
Improving the accuracy and adaptability of MLLMs for sentiment analysis can significantly enhance automated content moderation, market research, and intelligent assistant capabilities across various industries.
The proposed MAF framework introduces a dynamic, adaptive approach to prompt design for MLLMs, addressing a key limitation of static prompting and enabling more nuanced multimodal interpretation.
- · AI developers
- · Social media platforms
- · Customer analytics firms
- · E-commerce
- · Companies relying on static prompting
- · Manual sentiment analysis services
More accurate and context-aware sentiment analysis becomes achievable with MLLMs.
This could lead to improved automated decision-making in areas like content recommendations and brand reputation management.
Enhanced sentiment understanding at scale might indirectly influence consumer behavior and market trends through more targeted and adaptive AI interventions.
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Read at arXiv cs.AI