Question 1 of 30
A company is developing a generative AI model to classify customer feedback into various categories, such as product quality, service experience, and delivery issues. They have a limited dataset with only a few examples for each category. Additionally, they want the model to handle new categories that may arise in the future without retraining it extensively. Which approach would best suit their needs?
Implement few-shot learning to classify existing categories and utilize zero-shot learning for new categories.
Use traditional supervised learning with a large dataset to ensure high accuracy across all categories.
Rely solely on zero-shot learning to classify both existing and new categories without any prior examples.
Apply unsupervised learning techniques to cluster customer feedback into categories without predefined labels.

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