Transformers appeared in 2017 as a simple and scalable way to obtain SOTA results in language translation. They were soo

Here, the top row represents the words that are being processed and the lower row represents the words used as context (see that the words are the same but they are treated differently if they are being processed or used to process another word). See that “they”, “cool” or “efficient” in the top row have high weights pointing to “Transformer” since that is indeed the word that they are referencing.

Convolutional models have dominated the field of Computer Vision for years with tremendous success. Convolutions can be efficiently parallelized using GPUs and they provide suitable inductive biases when extracting features from images.

Given an input text with N words, for each word (W) Transformers create a N weights, one for every word (Wn) in the text. The value of each weight will depend on the dependency of the word in the context (Wn) to represent the semantics of the word that is being processed (W). The following image represents this idea. Note that the transparency of the blue lines represents the value of the assigned attention weights.

These weights are then used to combine the values from every pair of words and produce an updated embedding for each word (W) that now contains information about those important words (Wn) in the context for that word in particular (W).

I would recommend to take a look at the great post by Jay Alamar to those readers who want a more in-depth understanding of self-attention and of the Transformer model since this was a super shallow description of the most important parts of this technique.

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walk through a high-level overview of visualization grammar so that you, too, can start thinking compositionally about your charts. This particular framing is based on the ggplot2 interpretation of visualization grammar, but I’ve also taken some liberties to explain it concisely.

Self attention will compute attention scores between every pair of words in the text. The scores will be softmaxed, converting them into weights with a range between 0 and 1.

Each word embedding gets multiplied by a Key and a Query matrix resulting in the query and key representations of each word. To compute the score between W and Wn, the query embeddings of W (W_q) is “sent” to the key embeddings of Wn (Wn_k ) and both tensors are multiplied (using dot product). The resulting value of the dot product is the score between itself, it will represent how dependant W is with respect to Wn.

Note that we can use the second word as W and the first word as Wn as well, that way we would compute a score that would represent the dependency of the second word to understand the first. We can even use the same word as W and Wn to compute how important the word itself is for their definition!

It is common to train large versions of these models and fine-tune them for different tasks, so they are useful even when the data is scarce. Performances in these models, even with billions of parameters, do not seem to saturate. The larger the model, the more accurate the results are, and the more interesting the emerging knowledge that the model presents (see GPT-3).

See that each word embedding is now multiplied by a third matrix generating their value representation. This tensor will be used to compute the final embedding of each word. For each word W, the computed weights for each other word in the text Wn will be multiplied by their corresponding value representations (Wn_v) and they will be added altogether. The result of this weighted sum will be the updated embedding of the word W! (represented as e1 and e1 in the diagram).

Thanks to weight sharing, the features extracted from a convolutional layer are translation invariant, they are not sensitive to the global position of a feature, instead, they determine whether the feature is present or not.

Starting with the embedding of a word (W) from the input text, we need to somehow find a way to measure the importance of every other word embeddings (Wn) in the same text (importance with respect to W) and to merge their information to create an updated embedding (W’).

Under the hood, in order to compute these updated embeddings, transformers use self-attention, a highly efficient technique that makes it possible to update the embeddings of every word in the input text in parallel.

Self-attention will linearly project each word embedding in the input text into three different spaces, producing three new representations known as query, key and value. These new embeddings will be used to obtain a score that will represent the dependency between W and every Wn (high positive scores if W depends on W’ and high negative scores if W is not correlated with W’). This score will then be used to combine information from different Wn word embeddings, creating the updated embedding W’ for the word W.