How do modern LLMs achieve in-context learning without weight updates?

In-context learning emerges from pattern recognition in attention over prompt examples.

LLMs simulate learning by conditioning on examples provided in the prompt. The transformer learns to treat context tokens as temporary training data, effectively performing implicit meta-learning. This behavior emerges from training on diverse tasks, enabling the model to infer patterns without gradient updates.