Question
Which statement accurately describes the difference between a pretrained generative AI model and a fine-tuned generative AI model?
- A. A pretrained model requires labeled data, while a fine-tuned model does not.
- B. A pretrained model is faster to train than a fine-tuned model because the pretrained model uses fewer parameters.
- C. A pretrained model is trained on broad datasets, while a fine-tuned model is adapted to perform well on a narrower, domain-specific dataset. Correct
- D. A pretrained model is optimized for a specific task, while a fine-tuned model is designed for general-purpose use.
Explanation
A pretrained model is trained on broad data so that it works for general purposes. Fine-tuning gives that pretrained model additional training on a specific task or dataset, adapting it to a domain such as medicine, finance, or law. Option A is backwards, because fine-tuning is the step that needs prepared training examples, and option D reverses the two model types. Option B is wrong because a fine-tuned model starts from a pretrained model and has the same architecture, so parameter count does not separate them.
