What is the working principle of the Transformer architecture? How does its self-attention mechanism address long-range dependencies?

Okay, no problem. Let's talk about this topic in plain language.

The Transformer Architecture: More Than Just a 'Transformer'

You've probably heard of popular models like GPT and BERT. The unsung hero behind them is an architecture called Transformer. The name sounds cool, but what it does is even cooler.

What Exactly is a Transformer?

Before Transformer emerged, the mainstream method for processing sequential data like sentences was RNN (Recurrent Neural Network).

You can imagine an RNN as a 'single-core processor.' When reading a sentence, it processes one word at a time, in sequence. For example, with 'I love to eat apples,' it first reads 'I,' remembers its information, then reads 'love,' and combines it with the information from 'I' to understand, and so on.

This leads to two problems:

Low Efficiency: It has to process words one by one, without parallel processing, much like a single-core CPU running a large program – it's slow.
Poor Memory: When a sentence is very long, for example, 'This morning I saw a very cute cat outside my door, ..., it was really cute.' By the time the model reads 'it' at the end, it might have forgotten whether it was originally talking about a 'cat' or something else. This is known as the 'long-range dependency problem.'

The Transformer was designed to solve these problems!

It completely abandons the 'sequential processing' approach of RNNs. You can think of it as a 'multi-core processor.' When it receives a sentence, it processes all words simultaneously.

Its core structure is mainly divided into two parts: the Encoder and the Decoder.

Encoder: Responsible for 'understanding.' Like a master of reading comprehension, it reads the entire input sentence (e.g., a Chinese sentence), then extracts the deep meaning of each word and the relationships between them. Finally, it outputs a set of information-rich numbers (vectors), which can be seen as its 'summary of understanding' for the entire sentence.
Decoder: Responsible for 'generation.' It takes the 'summary of understanding' provided by the encoder and then generates a new sentence word by word (e.g., the translated English sentence). When generating each new word, it not only refers to the encoder's summary but also looks back at the words it has already generated to ensure coherence.

Encoder-Decoder (A classic Transformer architecture diagram)

Interlude: Positional Information Since all words are processed simultaneously, how does the model know the order of words? For example, 'cat chases dog' and 'dog chases cat' have completely different meanings. Transformer uses a clever method called Positional Encoding. Before feeding each word into the model, it attaches a 'position tag' to each word, telling the model its position in the sentence. This way, even with parallel processing, the model can still know the original word order of the sentence.

How Does Self-Attention Solve the 'Poor Memory' Problem?

This is the most core and ingenious design in Transformer, and it's key to its ability to handle long-range dependencies.

The word 'attention' is very vivid. When you read a sentence, your attention isn't evenly distributed. For example, in the sentence: 'The animal didn't cross the street because it was too tired,' when you read 'it,' your brain immediately focuses heavily on the word 'animal,' because you know 'it' refers to 'animal.'

The Self-Attention mechanism simulates this process.

Its working principle can be understood in simple terms as follows:

Establish Global Connections: For every word in the sentence, the self-attention mechanism makes it 'interact' with all other words in the sentence (including itself) once.
Calculate 'Attention Scores': During this 'interaction,' each word assigns an 'attention' or 'relevance' score to all other words. This score represents 'how much attention you should pay to that word to understand me.'
- In the example above, when the model processes the word it, it will assign a very high score to animal, and very low scores to words like street or cross.
Weighted Sum: Finally, the new meaning of each word is no longer its independent meaning. Instead, it's a 'weighted average' of the meanings of all words in the sentence, using these 'attention scores' as weights.
- This way, the new meaning of it contains a large amount of information from animal, allowing the model to accurately understand what it refers to.

How does it solve long-range dependencies?

The key lies in direct connections!

In an RNN, the information transfer between it and animal needs to pass through all intermediate words step by step. The longer the distance, the more information is lost, much like a game of telephone where the message gets distorted by the end.
In the self-attention mechanism, it and animal directly calculate their relevance, and a 'direct highway' can be established between them, no matter how far apart they are in the sentence. Distance is no longer an obstacle.

Therefore, by allowing any two words in a sentence to establish a direct connection and calculate their importance, the self-attention mechanism perfectly solves the long-range dependency problem, giving the model superior 'memory' and contextual understanding capabilities.

In Summary

The Transformer is a powerful model architecture that improves efficiency through parallel processing.
It uses the Self-Attention mechanism to understand the relationships between words in a sentence.
The Self-Attention mechanism establishes direct connections by calculating the 'attention' between any two words, thereby solving the 'poor memory' (long-range dependency) problem that traditional models (like RNNs) face when processing long texts.

I hope this explanation gives you a clear understanding of Transformer!