This research demonstrates that small language models (under 15B parameters) can outperform larger models for content moderation when properly fine-tuned, offering more efficient and community-specific approaches to online safety.

• Fine-tuned SLMs deliver superior performance for content moderation compared to larger models
• Enables community-specific moderation tailored to different online environments
• Provides cost-effective alternatives to expensive LLM inference in real-time moderation
• Creates more accessible security solutions for organizations with limited resources

This breakthrough matters for security teams by offering practical, affordable tools to identify and filter harmful content at scale while allowing customization to specific community standards and threat landscapes.

SLM-Mod: Small Language Models Surpass LLMs at Content Moderation