Attention Head

Attention heads, the fundamental building blocks of transformer-based models, are crucial for processing information in sequence data. Current research focuses on understanding their functional specialization during training, optimizing their efficiency for large language models (LLMs) through techniques like sparse attention and head clustering, and leveraging their internal representations for improved model interpretability and performance in various tasks. This work is significant because it addresses both the computational challenges of deploying LLMs and the need for better understanding and control over their internal mechanisms, ultimately leading to more efficient and effective AI systems.