This research advances implicit hate speech detection by improving how AI systems recognize subtle, context-dependent harmful content.

• Introduces a selective demonstration retrieval approach that chooses optimal examples for few-shot learning
• Achieves significant improvement in detecting implicit hate speech without additional training
• Creates a more robust detection system that better understands cultural and contextual nuances
• Demonstrates effectiveness across multiple datasets and language models

For security professionals, this research offers practical methods to identify harmful content that traditional systems often miss, helping create safer online communities while reducing false positives.

Selective Demonstration Retrieval for Improved Implicit Hate Speech Detection