Cracking the Code: How Large Language Models Perform Complex Tasks with In-Context Learning and Induction Heads Ahmet Münir Kocaman · Follow 7 min read · Just now Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP) by achieving remarkable feats in understanding and generating human language. However, the underlying mechanisms that enable these models to learn and perform specific tasks remain a mystery. In this article, we’ll dive into how these models accomplish their tasks, focusing on two key concepts: in-context learning (ICL) and induction heads. We’ll also explore how these mechanisms impact model performance and provide a sample code to guide these models toward specific tasks. Before we dive in, I’d like to express my gratitude to Joy Crosbie and Ekaterina Shutova for their […]
Original web page at medium.com
