The Evolution of Attention Mechanism in LLM
The evolution of attention mechanisms in large language models (LLMs) has significantly transformed natural language processing. Starting with local attention in LSTMs, the introduction of self-attention in the Transformer model marked a breakthrough, enabling parallelization and improved performance. Advancements include cross-attention for aligning sequences, hierarchical attention for processing complex structures, and techniques like attention calibration for optimizing accuracy. Attention mechanisms have also extended to multimodal tasks in speech and vision, and recent innovations address challenges like instruction forgetting and attention sinks. The future of attention mechanisms promises further integration across various domains, enhancing LLM capabilities.
