MAR-12: Multi-Angle Reasoning Framework for Detecting and Explaining Harmful Humor in Memes
Researchers have introduced MAR-12, a novel framework that leverages Vision Language Models to detect and explain harmful humor in memes by analyzing twelve structured perspectives based on humor and hate theories. MAR-12 achieves up to 80.3% accuracy for humor detection and 75.9% for hate detection on benchmark datasets, outperforming previous state-of-the-art methods. The system generates transparent, context-grounded explanations for its decisions, particularly in cases where humor and hate coexist. Human and GPT-4-based evaluations confirm the coherence and persuasiveness of its explanations.
Why it matters: This work advances explainable AI for multimodal content moderation, addressing the challenge of interpreting memes where humor and harmful intent overlap.
Full story at: arXiv AI/ML ↗