Attention mechanisms are techniques in NLP models that allow them to focus on specific parts of the input sequence while processing data. By assigning varying levels of importance (attention scores) to different words in a sequence, attention mechanisms help models understand context more effectively. For example, in the sentence "The bank by the river was beautiful," the model can use attention to associate "bank" with "river" to disambiguate its meaning.
The most well-known attention mechanism is self-attention, used in transformer models. Self-attention calculates the relationships between all words in a sequence, enabling the model to capture long-range dependencies and context. This is crucial for tasks like translation or summarization, where understanding the relationships between distant words is essential.
Multi-head attention extends self-attention by computing multiple sets of attention scores in parallel, allowing the model to focus on different aspects of the input. Attention mechanisms have largely replaced recurrent structures in modern NLP due to their efficiency and scalability, driving the success of models like BERT, GPT, and T5.