An attention mechanism is a technique in machine learning that allows a model to assign different levels of importance to parts of its input when producing an output. Instead of treating every piece of information equally, the model calculates which elements are most relevant to the current task and gives them greater influence. This helps models capture relationships between information that may be far apart in a sequence.
Attention became especially important in transformer-based models used for language, vision, and multimodal AI. For example, when processing a sentence, an attention mechanism can determine which words are most relevant to understanding a particular word or phrase. It is a core component of technologies such as large language models, machine translation systems, and many modern AI architectures.
