Attention in neural networks is a mechanism that allows models to dynamically focus on specific parts of input data when making predictions. Instead of treating all input elements equally, attention assigns different weights to them based on their relevance to the task. For example, in a sentence translation task, the model might pay more attention to the subject noun when generating the verb in the target language. This is achieved by computing a set of scores that determine how much each input element should influence the output. These scores are typically derived using learnable parameters and mathematical operations like dot products, followed by a softmax function to normalize them into probabilities. The result is a weighted sum of input features, which the model uses to make context-aware decisions.
A practical example of attention is the Transformer architecture, which relies heavily on self-attention. In self-attention, each element in a sequence (like a word in a sentence) computes its relevance to every other element. For instance, in the sentence “The cat sat on the mat,” when processing the word “sat,” the model might assign higher weights to “cat” (the subject) and “mat” (the location). This is done by creating three vectors for each input element: a query, a key, and a value. The query of “sat” is compared to the keys of all other words to determine their relevance. The values are then combined using these weights to produce a context-rich representation. Multi-head attention extends this by running multiple self-attention operations in parallel, allowing the model to capture different types of relationships (e.g., syntactic and semantic).
From an implementation perspective, attention improves efficiency and scalability. Unlike recurrent networks that process sequences step-by-step, attention enables parallel computation of all input relationships. Developers often implement attention using matrix operations, which are optimized for modern hardware like GPUs. For example, in PyTorch, the torch.nn.MultiheadAttention layer handles the computation of queries, keys, and values, applies masking (for tasks like language modeling), and returns the weighted outputs. Attention also addresses the limitation of fixed-context windows in earlier models, allowing networks to handle long-range dependencies more effectively. By focusing on relevant inputs dynamically, models become more interpretable—developers can visualize attention weights to understand which parts of the input influenced a prediction, aiding debugging and model improvement.