Self-attention is a mechanism that lets a model, when processing a sentence, first decide how much each word should attend to the other words in the sentence, and then fuse that information. It does not read word by word in a fixed order. Instead, all words score one another: the stronger the association, the larger the weight, and each word ends up with a new representation blended with the context it needs most.
A simple example
In “Xiaoming put the keys on the table, and he then went out,” who does “he” refer to? A reader can tell at a glance that it is Xiaoming. Self-attention does something similar: when the model processes “he,” it gives “Xiaoming” a higher weight and “table” and “keys” lower weights, so the representation of “he” carries information about Xiaoming. These weights are not hand-written rules; the model learns them from large amounts of text during training.
What it actually computes
The process can be broken into three steps: turn each word into a vector, compute matching scores between words, and then combine the other words' information weighted by those scores. High-scoring words have a large influence, while low-scoring words are almost ignored. The key point is parallelism: all words in a sentence can complete this step at the same time, rather than waiting for the previous word to finish, which is one reason it handles longer text and trains efficiently.
How it differs from recurrence and convolution
A recurrent structure reads a sentence in order, so information from distant words tends to weaken as it is passed along. A convolutional structure looks only at a fixed window of words at a time, and relations outside the window can only be built indirectly by stacking layers. Self-attention has no fixed window and does not rely on sequential passing, so distant words can connect directly. It is not omnipotent, however: it needs extra position information to know word order, otherwise “the cat chases the dog” and “the dog chases the cat” would be hard to tell apart.
Where its limits lie
Self-attention only decides which information matters more. It does not memorize facts, perform logical reasoning, or guarantee correct output. Its visible range is also limited by context length: content beyond the window cannot take part directly, and computation grows quickly as sentences lengthen, so long text costs noticeably more than short text. It is more accurate to think of it as an information filter than as a knowledge base or reasoning engine.
Common misunderstandings
First, self-attention is not understanding itself: a high weight only means a strong statistical association learned by the model, not that it truly grasps meaning. Second, attention weights cannot be used directly as an explanation: a word with a high weight is not necessarily the sole reason for a particular answer. Third, self-attention is not the whole Transformer, which also includes feed-forward networks, positional encodings, normalization, and other parts; attention alone cannot support a complete model.
