What is the difference between in-context learning and fine-tuning in LLMs?

A In-context learning requires gradient updates; fine-tuning does not B In-context learning (few-shot prompting) adapts behavior without parameter updates; fine-tuning updates model weights on task-specific data C Fine-tuning is more efficient at inference time D In-context learning is for smaller models; fine-tuning for larger