Overfitting in machine learning models can be caused by several things, including:

1. Too many parameters—if your model has many more parameters than it needs, it may learn to "cheat" its assigned task by simply "memorizing" the training data instead of learning generalizable principles.
2. Too little data—if your dataset is small, sparse, or particularly low-variance, it may be easier for your model to overfit to it. Adding more data to your training set and using data augmentation can help offset this.
3. Lack of regularization—techniques such as Dropout can help models learn general principles instead of memorizing data. While not always a silver bullet, it may be worth experimenting with integrating popular model regularization techniques into your training regiment.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence