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Transformer Models
Description:
Tokenization is the most basic step. It consists of a large dataset of tokens, including all the words, punctuation signs, etc. The tokenization step takes every word, prefix, suffix, and punctuation signs, and sends them to a known token from the library.
For example, if the sentence is “Write a story”, then the 4 corresponding tokens will be
<Write>,<a>,<story>, and\<>.Once the input has been tokenized, it’s time to turn words into numbers. For this, we use an embedding. In a previous chapter, you learned about how text embeddings send every piece of text to a vector (a list) of numbers. If two pieces of text are similar, then the numbers in their corresponding vectors are similar to each other (componentwise, meaning each pair of numbers in the same position is similar). Otherwise, if two pieces of text are different, then the numbers in their corresponding vectors are different.
For example, if the sentence we are considering is “Write a story.” and the tokens are
<Write>,<a>,<story>, and\<>, then each one of these will be sent to a long vector, and we’ll have four vectors.In general embeddings send every word (token) to a long list of numbers.
Once we have the vectors corresponding to each of the tokens in the sentence, the next step is to turn all these into one vector to process. The most common way to turn a bunch of vectors into one vector is to add them, componentwise. That means, we add each coordinate separately. For example, if the vectors (of length 2) are [1,2], and [3,4], their corresponding sum is [1+3, 2+4], which equals [4, 6]. This can work, but there’s a small caveat. Addition is commutative, meaning that if you add the same numbers in a different order, you get the same result. In that case, the sentence “I’m not sad, I’m happy” and the sentence “I’m not happy, I’m sad”, will result in the same vector, given that they have the same words, except in different order. This is not good. Therefore, we must come up with some method that will give us a different vector for the two sentences. Several methods work, and we’ll go with one of them: positional encoding. Positional encoding consists of adding a sequence of predefined vectors to the embedding vectors of the words. This ensures we get a unique vector for every sentence, and sentences with the same words in different order will be assigned different vectors. In the example below, the vectors corresponding to the words “Write”, “a”, “story”, and “.” become the modified vectors that carry information about their position, labeled “Write (1)”, “a (2)”, “story (3)”, and “. (4)”.
Positional encoding adds a positional vector to each word, in order to keep track of the positions of the words.
Now that we know we have a unique vector corresponding to the sentence, and that this vector carries the information on all the words in the sentence and their order, we can move to the next step.
Let’s recap what we have so far. The words come in and get turned into tokens (tokenization), tokenized words are turned into numbers (embeddings), then order gets taken into account (positional encoding). This gives us a vector for every token that we input to the model. Now, the next step is to predict the next word in this sentence. This is done with a really really large neural network, which is trained precisely with that goal, to predict the next word in a sentence.
We can train such a large network, but we can vastly improve it by adding a key step: the attention component. Introduced in the seminal paper Attention is All you Need, it is one of the key ingredients in transformer models, and one of the reasons they work so well. Attention is explained in the previous section, but for now, imagine it as a way to add context to each word in the text.
The attention component is added at every block of the feedforward network. Therefore, if you imagine a large feedforward neural network whose goal is to predict the next word, formed by several blocks of smaller neural networks, an attention component is added to each one of these blocks. Each component of the transformer, called a transformer block, is then formed by two main components:
The transformer is a concatenation of many transformer blocks. Each one of these is composed by an attention component followed by a feedforward component (a neural network).
The next step is attention. As you learned in the previous chapter, the attention mechanism deals with a very important problem: the problem of context. Sometimes, as you know, the same word can be used with different meanings. This tends to confuse language models, since an embedding simply sends words to vectors, without knowing which definition of the word they’re using.
Attention is a very useful technique that helps language models understand the context. In order to understand how attention works, consider the following two sentences:
Sentence 1: The bank of the river. Sentence 2: Money in the bank.
As you can see, the word ‘bank’ appears in both, but with different definitions. In sentence 1, we are referring to the land at the side of the river, and in the second one to the institution that holds money. The computer has no idea of this, so we need to somehow inject that knowledge into it. What can help us? Well, it seems that the other words in the sentence can come to our rescue. For the first sentence, the words ‘the’, and ‘of’ do us no good. But the word ‘river’ is the one that is letting us know that we’re talking about the land at the side of the river. Similarly, in sentence 2, the word ‘money’ is the one that is helping us understand that the word ‘bank’ is now referring to the institution that holds money.
Attention helps give context to each word, based on the other words in the sentence (or text).
In short, what attention does is it moves the words in a sentence (or piece of text) closer in the word embedding. In that way, the word “bank” in the sentence “Money in the bank” will be moved closer to the word “money”. Equivalently, in the sentence “The bank of the river”, the word “bank” will be moved closer to the word “river”. That way, the modified word “bank” in each of the two sentences will carry some of the information of the neighboring words, adding context to it.
The attention step used in transformer models is actually much more powerful, and it’s called multi-head attention. In multi-head attention, several different embeddings are used to modify the vectors and add context to them. Multi-head attention has helped language models reach much higher levels of efficacy when processing and generating text.
More about Transformer Models
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