This research introduces improved fine-tuning techniques for large multimodal models to better detect harmful meme content across evolving social contexts.

• Addresses key limitation: Conventional fine-tuning of large multimodal models fails to generalize well for hateful meme detection
• Novel approach: Enhances model adaptability to emerging social trends and breaking news contexts
• Security application: Provides more robust automated detection systems for identifying harmful content online
• Practical impact: Helps platforms better moderate content and protect users from exposure to hateful material

This advancement offers significant potential for improving online safety mechanisms through more accurate detection of harmful multimodal content.

Improved Fine-Tuning of Large Multimodal Models for Hateful Meme Detection