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What is Zero-Shot Learning?

A machine learning approach where models perform tasks without having seen labeled examples for those tasks during training.

More about Zero-Shot Learning:

Zero-Shot Learning allows AI models to handle tasks they haven’t been explicitly trained on by leveraging pre-trained knowledge from large datasets. This capability is achieved through transfer learning, enabling models like GPT or BERT to generalize to new domains or tasks.

Zero-shot learning is integral to systems like retrieval-augmented generation (RAG) and knowledge retrieval, where understanding and processing unseen queries is critical.

Frequently Asked Questions

What are the benefits of zero-shot learning?

It eliminates the need for task-specific labeled data, enabling faster deployment of AI systems in new domains.

What are common applications of zero-shot learning?

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