17 Amazing Facts You Must Know About TRANSFORMERS [Paper Unfold]

Paper Unfold is a series in which you will get a breakdown of complex research papers into easy-to-understand pointers.

You already know about this famous paper Attention Is All You Need by Google.

In simple terms,

Earlier in the day, you need to learn the binary language to communicate with the machine.

Later we came up with a programming language, you can learn Python and give instructions to the machine.

But now you can directly provide instructions in English.

The gap between humans and machines is reduced, you don’t need to learn programming to interact with a machine.

Here are 17 facts you must know about transformers

1. transformers are a type of neural network that use attention mechanisms, eliminating the need for recurrence or convolutions, making training much faster

2. they are great at tasks like language translation and modeling, often beating older models

3. the key part of transformers is the scaled dot-product attention, which calculates the relevance of words in context
   (It figures out which words are important in a sentence by comparing each word to others in the context)

4. multi-head attention, which lets it focus on different parts of the input at the same time, like reading a story and paying attention to characters, plot, and setting all at once.

5. transformers use multi-head attention in three main ways:

   1. encoder-decoder attention connects the input and output sequences

   2. encoder self-attention lets the model learn from all input words at once

   3. decoder self-attention ensures the output words are generated step by step

6. they use simple feed-forward layers at each position to make the model better at finding patterns and meaning

7. transformers turn words into numbers, called embeddings, which the model uses to understand the relationships between words

8. transformers don’t read text word by word, they need "positional encodings" to understand the order of words in a sentence, like knowing “The cat chased the dog” is different from “The dog chased the cat.”

9. because of self-attention, it can quickly connect words that are far apart, unlike older methods that struggle with long-range relationships

10. the way attention works can also make the model's decisions easier to interpret

11. during training, the transformer learns how to break down text into smaller chunks (tokens) efficiently, which speeds up the process

12. training was done using 8 NVIDIA P100 GPUs with special optimization techniques

13. it sets new records for translation accuracy while requiring less training time

14. experiments showed that attention heads, model size, and dropout are key to good performance

15. the model works well on tasks outside of translation, breaking down complex sentences into their grammatical parts

16. it even beat rnn-based models on smaller datasets

17. making output generation less dependent on previous steps could make these models even faster

What problem does the Transformer architecture solve?

It solves the problem of handling sequences more efficiently.

Recurrent models, such as RNNs and LSTMs, process sequences one step at a time, which makes training slow and hard to parallelize, especially for long sequences.

Memory constraints also make it difficult to handle multiple examples at once in these cases.

Convolutional models, like ConvS2S and ByteNet, can process sequences in parallel, but they struggle with capturing relationships between distant elements.

The Transformer solves these problems using attention mechanisms instead of recurrence or convolution.

The self-attention mechanism also makes it easy for the model to learn connections between distant parts of a sequence in a fixed number of steps.

It enables greater parallelization and efficient handling of long-range dependencies.

What is one thing you wish more AI newsletters covered?

Reply to this email.