How do I overcome model degradation in Generative AI models when training on non-ideal datasets like noisy text data

Question

Can i know How do I overcome model degradation in Generative AI models when training on non-ideal datasets like noisy text data?

Answer 1

To overcome model degradation in Generative AI models trained on noisy text data, use data cleaning, adversarial training, and robust loss functions to enhance model resilience and maintain performance.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Noise Reduction – Uses regex-based preprocessing to clean noisy text (removes URLs, special characters, and extra spaces).
• Adversarial Training – Introduces controlled perturbations in training data to make the model more robust.
• Data Augmentation – Generates variations of training text to improve generalization.
• Fine-Tuning for Error Correction – Enhances model adaptability to imperfect datasets.
• Scalability – Can be extended with semi-supervised learning or self-correction techniques for better performance.

Hence, overcoming model degradation in Generative AI requires data cleaning, adversarial training, and robust loss functions to improve resilience against noisy text datasets and maintain model quality.

Related Post: Effective data preprocessing techniques for noisy and incomplete datasets