Instruction: Explain the process and importance of fine-tuning in customizing LLMs for specific tasks or datasets.
Context: This question evaluates the candidate's understanding of how LLMs are adapted from their general form to perform specialized functions.
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The way I'd explain it in an interview is this: Fine-tuning adapts a pretrained model to a narrower task, style, or domain. Pretraining gives the model broad language ability, but fine-tuning can make it more useful for things like customer support, code tasks, classification, or domain-specific...