arXiv:2606.03604v1 Announce Type: new Abstract: When asked what a meme or sarcastic post means, Large Vision Language Models (LVLMs) tend to describe what the image shows rather than what the author is trying to communicate. Standard instruction tuning entangles a post's literal content with its pragmatic meaning, letting surface-level details contaminate the final response. We reframe meme understanding as a problem of literal-pragmatic decomposition and propose \textbf{Intent Projection}, a framework that separates the two signals at the representation, output, and objective levels within a
The rapid advancement of Large Vision Language Models necessitates deeper understanding of their limitations in interpreting complex human communication beyond literal content.
Improving AI's ability to understand pragmatic intent in multimodal content like memes is crucial for more sophisticated and nuanced human-AI interaction, impacting everything from content moderation to personalized assistants.
This research proposes a new framework for AI to decompose literal and pragmatic meaning, moving AI systems closer to understanding the 'why' behind human communication rather than just the 'what'.
- · AI developers
- · Social media platforms
- · Content moderation services
- · AI-driven marketing
- · Platforms reliant on superficial AI content analysis
- · AI models without pragmatic understanding
AI models will gain an improved ability to interpret irony, sarcasm, and cultural references in visual and textual content.
More sophisticated AI agents could be developed that are better at understanding complex user prompts and social cues, leading to more human-like interactions.
This could enable hyper-personalized content generation and moderation systems that are sensitive to cultural and contextual nuances, potentially reshaping digital communication norms.
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Read at arXiv cs.CL