The Evolution of Attention Mechanism in LLM
Attention mechanisms have evolved significantly in natural language processing, starting from local attention in LSTMs to self-attention in Transformers, which improved parallelization and performance. Recent advancements include cross-attention for aligning different sequences, tree-based hard attention for hierarchical structures, and techniques for optimizing attention distribution during inference. Applications extend to multimodal tasks like speech recognition and cybersecurity, highlighting the versatility of attention mechanisms. Challenges such as instruction forgetting and attention sinks remain, but ongoing innovations suggest a promising future for attention mechanisms in enhancing model efficiency and effectiveness across various applications.