To improve LLM moderation accuracy, a proposed system requires models to output valid JSON, routing uncertain classifications to human review and reserving automatic blocks for high-confidence violations. This approach aims to reduce false positives by distinguishing between category confidence and severity, and by considering context for sensitive topics like medical terms or slang. The system suggests using a three-way decision process (allow, review, block) and testing with a diverse set of fixtures to ensure models adhere to output contracts and policy decisions. AI
IMPACT Proposes a system to improve LLM moderation accuracy and reduce false positives, impacting content filtering and user experience.
RANK_REASON The item discusses a proposed system for LLM moderation and its policy implications, rather than announcing a new product or research.
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