QuestionQ69

Core Machine Learning and AI Knowledge

In an NLP application, which approach is most effective for using zero-shot learning to classify text data into categories not encountered during training?

  • A Train the new model from scratch for each new category encountered.
  • B Use rule-based systems to manually define the characteristics of each category.
  • C Use a pre-trained language model with semantic embeddings.
  • D Use a large, labeled dataset for each possible category.
Explanation

A pre-trained language model with semantic embeddings can represent both text and unseen category descriptions by meaning, allowing classification through semantic similarity without labeled training examples for those categories.

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