LLMs
Attention
Attention is a mechanism that allows neural networks to focus on the most relevant parts of the input data when making predictions. It mimics cognitive attention, enabling the model to prioritize certain input features over others based on their importance to the current task.
Explanation
In the context of neural networks, attention mechanisms assign weights to different parts of the input sequence, indicating their relevance to the output. These weights are learned during training, allowing the model to automatically determine which parts of the input are most important. For example, in machine translation, attention helps the model focus on the specific words in the source sentence that are most relevant to generating the next word in the target sentence. The attention mechanism typically involves calculating a score for each input element, often using a function of the input element and the current hidden state of the model. These scores are then normalized (e.g., using a softmax function) to produce weights that sum to one. These weights are then used to compute a weighted sum of the input elements, which is then used as input to the next layer of the network. Attention is a key component of Transformer networks, enabling them to process sequential data in parallel and capture long-range dependencies more effectively than recurrent neural networks. Different variations of attention mechanisms exist, including self-attention (where the input attends to itself) and cross-attention (where the input attends to a different input).