Attention mechanisms allow LLMs to focus on the most relevant parts of the input when processing text. They work by assigning weights to different tokens in a sequence, indicating their importance relative to the task. For instance, in the sentence “The cat sat on the mat, and it purred,” attention mechanisms help the model link “it” to “cat.”
Self-attention, a specific type of attention used in transformers, enables the model to analyze relationships within a sequence. Each token attends to all other tokens, capturing both local and global context. This is achieved through mathematical operations that compute attention scores and weights, which are then applied to the input tokens.
Attention mechanisms are essential for understanding dependencies in language, such as subject-verb agreement or contextual meaning. They also allow LLMs to process text in parallel, making them more efficient than older sequential models like RNNs. This innovation is a key reason for the success of LLMs in NLP tasks.