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?
Applications include semantic search, question answering, and retrieval fusion.
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