Introducing the transformer

1. Introducing the transformer

Let's learn about transformers and how they help strengthen LLMs.

2. Where are we?

Transformers are part of pre-training and enhance the techniques we have already learned about.

3. What is a transformer?

It all started with the release of the “Attention Is All You Need” research paper that changed how language modeling is done today. The transformer architecture emphasizes long-range relationships between words in a sentence to generate accurate and coherent text. It has four essential components: pre-processing, positional encoding, encoders, and decoders.

4. Inside the transformer

Let's consider an input text, "Jane, who lives in New York and works as a software". The transformer pre-processes input text, converting it to numbers and incorporating position references. The encoder uses this information to encode the sentence, which the decoder then uses to predict subsequent words. The predicted word is added to the input, and the process continues until the final output completes the input sentence. Here, the final output is "engineer, loves exploring new restaurants in the city". Let's walk through these steps.

5. Transformers are like an orchestra

Imagine the transformer as an orchestra.

6. Text pre-processing and representation

The first component is text pre-processing and representation where the transformer breaks down the sentences into individual tokens, like a composer separating the music into individual notes. Recall that tokenization breaks sentences into tokens and that stop word removal and lemmatization are also text pre-processing techniques. These tokens then need to be presented in numerical form using word embeddings, a text representation technique. This is similar to sheet music providing a set of instructions to musicians to interpret and play.

7. Positional encoding

The second component is positional encoding, which provides information about the position of words in a sequence, helping a transformer tie together distant words. This is similar to understanding the relationships between distant notes that create a coherent piece of music.

8. Encoders

The third component, encoders, includes the attention mechanism and neural network. The attention mechanism directs attention to specific words and their relationships. In music, this is similar to musicians adjusting their volume in specific sections. We'll explore this mechanism more in the next video. Recall that neural networks are algorithms inspired by the human brain. The different layers of neural networks process specific features of the input data to interpret complex patterns and pass them to the next layer, just as each musician contributes to the final musical piece.

9. Decoders

The decoders, the fourth component, also use attention and neural networks to process the encoded input and generate the final output. This is similar to how individual musicians combine their knowledge as an orchestra to create a cohesive and meaningful performance. Great, so we understand how transformers work. Let's check out what makes them special.

10. Transformers and long-range dependencies

Recall that long-range dependencies require capturing relationships between distant words in a sentence - which can be challenging to model. The transformer's attention mechanism overcomes this limitation by focusing on different parts of the input. Going back to our previous example: "Jane, who lives in New York and works as a software engineer, loves exploring new restaurants in the city", LLMs can attend to the relationship between the distant words - "Jane" and "loves exploring new restaurants", leading to better contextual understanding.

11. Processes multiple parts simultaneously

When handling language, traditional language models are sequential, meaning they process one word at a time. Transformers are an improvement in this area, because they focus on multiple parts of the input text simultaneously, speeding up the process of understanding and generating text. For example, in the sentence "The cat sat on the mat," transformers can process "cat," "sat," "on," "the," and "mat" at the same time.

12. Let's practice!

Now it's time to check our understanding.

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