In-context learning happens when a machine learning model can perform a task successfully after a short number of attempts, even though the information was not included in model's training data.Researchers at MIT, Stanford University, and Google conducted experiments to find out more about the process, and were able to show how machine learning language models inherently create smaller models within themselves when performing in-content learning.
