In-context Learning and Induction Heads

This post introduces the concept of 'induction heads' within neural networks, drawing parallels to how they might function in LLMs. It proposes that these induction heads are responsible for in-context learning by identifying and applying patterns from the prompt. The research suggests that these heads operate by learning to predict the next token based on observed patterns, effectively acting as a form of meta-learning within the model. The post details experiments showing how these heads can be identified and how their behavior correlates with the model's ability to perform in-context learning tasks. This work lays the groundwork for understanding the internal mechanisms of LLMs and how they learn from context.