In-Context Learning

In-context learning is one of the most striking properties of large language models. Without changing its parameters, the model can infer task behavior from instructions and examples placed in the prompt. This has blurred the classical distinction between training and inference and has fundamentally changed how NLP applications are built. However, prompt quality and example selection remain decisive factors for performance.