CHAIRO: Contextual Hierarchical Analogical Induction and Reasoning Optimization for LLMs

ArXi:2604.10502v1 Announce Type: new Content moderation in online platforms faces persistent challenges due to the evolving complexity of user-generated content and the limitations of traditional rule-based and machine learning approaches. While recent advances in large language models (LLMs) have enabled sophisticated moderation via direct prompting or fine-tuning, these approaches often exhibit limited generalization, interpretability, and adaptability to unseen or ambiguous cases.