Self-attention

Self-attention is a mechanism in large language models (LLMs) that allows the model to attend to different parts of the input sequence. This is important because the meaning of a word can change depending on its context. For example, the word "bank" can mean a financial institution or the side of a river. Self-attention allows the model to learn the context of each word in the input sequence and use this information to generate the output.

Self-attention works by first computing a score for each pair of words in the input sequence. This score measures how relevant each word is to the other words. The scores are then used to create a weighted sum of the word embeddings, where the weights are determined by the scores. This weighted sum is then used as the representation of the input sequence.

Self-attention is a powerful mechanism that has been shown to be very effective for a variety of NLP tasks. It has been used for machine translation, text summation, question answering, and many other tasks.

Here is an example of how self-attention can be used to understand the meaning of a sentence. Let's say we have the sentence "The cat sat on the mat." The self-attention mechanism would first compute a score for each pair of words in the sentence. The score for the pair "The cat" would be high, as these two words are closely related. The score for the pair "mat" and "on" would also be high, as these two words are also closely related. The scores for the other pairs of words would be lower.

The self-attention mechanism would then create a weighted sum of the word embeddings, where the weights are determined by the scores. The weighted sum would be the representation of the sentence. This representation would capture the meaning of the sentence, including the relationship between the words.

Self-attention is a powerful mechanism that has made a significant contribution to the development of LLMs. It has allowed LLMs to learn long-range dependencies and context, which has made them more capable of understanding and generating natural language.