Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuning

The author argues that incorporating reasoning processes during fine-tuning of Large Language Models can enhance model robustness and overcome overfitting, even without directly outputting reasoning processes during deployment.