Attention Mechanism

An Attention Mechanism is a component of a Neural Network that dynamically weights and combines information from different input positions or internal representations, helping capture Long-Range Dependencies. In Neural Machine Translation, attention was introduced by Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio in 2014, allowing models to selectively access input representations rather than rely on a single fixed-length vector encoding an entire sequence. Attention can operate over words, image patches, or other elements, but does not by itself solve the Vanishing Gradient Problem in Recurrent Neural Networks or Long Short-Term Memory networks. A prominent variant, Self-Attention, relates positions within the same sequence and is central to the Transformer architecture, introduced by researchers at Google in 2017. Transformer attention uses Query-Key-Value representations: a Compatibility Function compares queries with keys to produce scores, which are normalized into Attention Weights used to compute a Weighted Sum of values. By replacing sequential recurrence with attention-based processing, Transformers enable parallel computation across sequence positions during training. They underpin many Large Language Models developed by organizations such as OpenAI, Google DeepMind, and Anthropic, contributing substantially to advances in Natural Language Processing.