Source: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Introduction In-context learning has emerged as one of the most remarkable capabilities of large language models like GPT-3 and GPT-4. With just a few demonstration examples, these models can rapidly adapt to new tasks and make accurate predictions without any parameter updates. But how does this impressive on-the-fly learning actually work behind the scenes? In a fascinating new paper from Microsoft Research and Peking University, researchers provide new theoretical insights that help unravel the optimization processes underlying in-context learning in Transformer models. By drawing parallels to gradient descent and analyzing the mechanics