Understanding Attention in Large Language Models

This chapter explores the critical role of attention mechanisms in large language models (LLMs), detailing how tokens interact through key, query, and value vectors. It discusses the computational challenges of traditional attention models, introduces mixed attention strategies, and evaluates trade-offs in model architecture. Additionally, the importance of effective evaluation metrics and methods for assessing long context capabilities are highlighted, demonstrating the complexity of optimizing language models.