What does zero-shot learning in Generative AI allow a model to do?

What does zero-shot learning in Generative AI allow a model to do?

• A. Make predictions with extra training examples
• B. Make predictions on previously unseen classes
• C. Improve accuracy on known classes
• D. Reduce the need for data labeling

Answer: (B)

Zero-shot learning in Generative AI enables a model to generalize and make predictions on previously unseen classes without requiring additional training examples for those specific classes. This capability is particularly powerful because it allows the model to apply knowledge acquired from one set of tasks or categories to new, unencountered tasks or categories. For instance, if a model has been trained on identifying various types of animals, it can use its understanding of animal characteristics to recognize a type of animal it has never seen before, simply based on descriptions or related attributes. In contrast, making predictions with extra training examples typically refers to approaches like transfer learning or fine-tuning, where models are trained further on a new dataset. Improving accuracy on known classes pertains to enhancing the performance for categories the model is already familiar with, which is not the essence of zero-shot learning. Reducing the need for data labeling is more aligned with concepts like weak supervision or semi-supervised learning rather than zero-shot learning, which focuses on the model's ability to understand and interpret entirely new categories based on existing knowledge or context.

Zero-shot learning in Generative AI enables a model to generalize and make predictions on previously unseen classes without requiring additional training examples for those specific classes. This capability is particularly powerful because it allows the model to apply knowledge acquired from one set of tasks or categories to new, unencountered tasks or categories. For instance, if a model has been trained on identifying various types of animals, it can use its understanding of animal characteristics to recognize a type of animal it has never seen before, simply based on descriptions or related attributes.

In contrast, making predictions with extra training examples typically refers to approaches like transfer learning or fine-tuning, where models are trained further on a new dataset. Improving accuracy on known classes pertains to enhancing the performance for categories the model is already familiar with, which is not the essence of zero-shot learning. Reducing the need for data labeling is more aligned with concepts like weak supervision or semi-supervised learning rather than zero-shot learning, which focuses on the model's ability to understand and interpret entirely new categories based on existing knowledge or context.