[AI from Scratch] Episode 193: The Internal Structure of GPT Models — A Detailed Look at the GPT Series

Recap: Details of Text Generation Models

In the previous episode, we explored text generation models in depth. We learned how models like Sequence-to-Sequence, RNNs (Recurrent Neural Networks), and Transformers automatically generate natural text. Transformers, in particular, have become the dominant approach due to their computational efficiency and ability to handle long texts. This time, we will focus on one of its applications, the GPT (Generative Pre-trained Transformer) series, examining its internal structure.

What Is a GPT Model?

GPT (Generative Pre-trained Transformer) is a high-performing language model used for text generation tasks in natural language processing (NLP). Built upon the Transformer architecture, GPT leverages a two-step learning process involving pre-training and fine-tuning to handle various tasks effectively.

The GPT series has three primary features:

1. Pre-training: Using a large corpus of text data, GPT models learn general language knowledge through self-supervised learning.
2. Fine-tuning: The model is then adjusted for specific tasks, enabling high-precision results.
3. Autoregressive Generation: The model predicts the next word sequentially, using previous words to generate coherent text.

The Architecture of GPT Models

GPT is based on the Decoder part of the Transformer architecture. Below are the main components of the GPT architecture:

1. Tokenizer

The GPT model processes input text by breaking it into smaller units called tokens. The tokenizer is the component responsible for this process. By converting text into tokens, the model better understands the basic elements of the language.

Example: How the Tokenizer Works

For example, if the Japanese sentence “私は学生です” (“I am a student”) is input into the tokenizer, it breaks it into tokens like “私” (“I”), “は” (a particle), “学生” (“student”), and “です” (“am”). These tokens form the foundation for the model to analyze language and predict the next word.

2. Embedding Layer

The tokenized data is then transformed into numerical vectors through the embedding layer. This layer is crucial for capturing relationships and contextual information between tokens.

Example: The Role of the Embedding Layer

If the token “student” has a similar meaning to tokens like “study” or “school,” their numerical vectors will be close in value. The embedding layer thus creates numerical representations that reflect the semantic information of the language.

3. Attention Mechanism

GPT uses the self-attention mechanism to focus on important parts of the input tokens. This mechanism highlights significant words or phrases, enabling the model to make predictions based on relevant context.

Example: How Self-Attention Works

In the sentence “He scored in yesterday’s match,” the relationship between “He” and “scored” is crucial. The attention mechanism emphasizes these words to make contextually appropriate predictions.

4. Masked Self-Attention

GPT uses masked self-attention to predict the next word based only on the already generated words. This prevents the model from referencing future information, ensuring that the text is generated in a natural order.

Example: Importance of Masked Self-Attention

When the model receives input like “I am currently,” it only considers “I” and “am” when predicting the next word, ignoring future information.

5. Residual Connections and Layer Normalization

GPT employs residual connections in each layer to prevent information loss and stabilize the learning process. Additionally, layer normalization is applied to adjust the model for efficient learning.

Example: The Role of Residual Connections

Residual connections add the output directly back to the input, ensuring that information is smoothly transformed and reducing the risk of information loss during learning.

Evolution of the GPT Series

GPT-1

The first GPT model, GPT-1, used a relatively small dataset for self-supervised learning, enabling it to acquire general language knowledge. However, its adaptability to specific tasks was limited.

GPT-2

GPT-2 was trained on a much larger dataset, allowing for higher precision in text generation. It significantly improved in maintaining coherence and generating long texts, showing strong performance across various tasks.

GPT-3

GPT-3 expanded even further, utilizing 175 billion parameters. This model can handle more complex tasks and generates text remarkably similar to human language. It excels in tasks such as question answering, dialogue, and summarization.

Applications of GPT

1. Building Natural Dialogue Systems

GPT is used in dialogue systems like chatbots and voice assistants. It enables natural conversations and can provide appropriate responses to user inquiries.

2. Text Summarization

GPT is also effective for summarization tasks. It extracts key information from input text to generate concise summaries.

3. Creative Writing

GPT assists in creative writing tasks like generating poetry, stories, and novels. It follows user prompts to continue storylines or generate character dialogues.

Summary

In this episode, we explored the internal structure of GPT models. GPT is based on the decoder part of the Transformer architecture and utilizes techniques such as the self-attention mechanism and masked self-attention to generate coherent and consistent text. The GPT series has evolved from GPT-1 to GPT-3, expanding its capabilities to handle more complex tasks. In the next episode, we will delve into the multi-head attention mechanism, a core component of the Transformer model.

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Preview of the Next Episode

Next time, we will explain the multi-head attention mechanism. This technique is a critical aspect of the Transformer model and enables understanding context from multiple perspectives. Stay tuned!

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Annotations

1. Tokenizer: The process of breaking down text into smaller units called tokens.
2. Embedding Layer: A layer that converts tokens into numerical vectors, reflecting semantic information.
3. Self-Attention Mechanism: A method to focus on important words or phrases in a sentence to make contextually appropriate predictions. In GPT, this mechanism captures relationships within the context while generating the next word.
4. Masked Self-Attention: In GPT, this approach ensures that only past information is used for predicting the next word, maintaining the natural sequence of text.