Bad teacher bots can leave hidden marks on model students

1. 1Audit your training data for potential biases
2. 2Use techniques like data preprocessing and regularization to mitigate biases
3. 3Test your model for fairness and robustness using metrics like accuracy and F1-score
4. 4Use explainability methods to understand how your model is making predictions
5. 5Implement human oversight and review to detect and correct biases

AI engineers and researchers working with LLMs should be aware of this issue to ensure their models are fair and unbiased, and to avoid perpetuating harmful stereotypes

💡 Biases can be transmitted from teacher to student model through output, highlighting the need for careful model selection and auditing

🚨 Bad teacher bots can smuggle biases into model students, even if scrubbed from training data! 🚨