Curiosity is All You Need: Learn How GPT Was Created in Just a Few Minutes

GPT or Generative Pre-trained Transformer generates human-like text by predicting the next word in a sequence based on the provided context. Its development evolves through four stages:

1. Pretraining: Collect vast amounts of internet text and transform it into tokens for GPT to predict the next token in a sentence.
2. Supervised Fine-Tuning: Train GPT with question-answer pairs to improve accuracy and helpfulness in generating complete responses.
3. Reward Modeling: Use human feedback to assign higher rewards to preferred answers, guiding GPT to choose the best responses.
4. Reinforcement Learning: Refine GPT's responses through continuous feedback, maximizing high-reward interactions over time.

Let's briefly explain what happens at each of these stages:

1. Pretraining: Learning from the Internet

• Data Collection: The first step is collecting vast amounts of text from the internet, including books, articles, and other resources. Some popular sources include CommonCrawl, Wikipedia, and other public datasets.

• Tokenization: All this text (sentences or chunks of words) gets turned into numbers or tokens. Why? It's easier for computers. Transforming complex content into manageable numerical tokens, enables easier processing, analysis, and improved model performance.

• Learning Patterns: GPT learns by predicting the next token in a sentence. For example, if it reads "To be or not to...", it learns that "be" might come next. Through billions of these predictions, GPT gets better at understanding language. Think of how children learn to speak. When children hear the same statements over and over, they start to understand which words follow each other. However, GPT (at the time of writing this article) only guesses the next token and, unlike a human child, doesn't truly understand or reason. Guessing, Really!!!?. Yes, this is how it works. GPT predicts the next word to complete a sentence based on the provided context.

Here are two important terms to understand at this stage:

• Transformers: The engines behind GPT. They analyze the tokens and determine the context, using this information to predict what token comes next.
• Positional encoding: A technique that gives GPT a sense of word order, which is crucial since the order in which words appear can completely change their meaning.

2. Supervised Fine-tuning: Becoming an Assistant

With pretraining alone, we only get a text completion program. Pretraining teaches GPT to predict the next word in a sentence based on vast amounts of text from the internet. This results in a model that's good at completing sentences but not necessarily at answering questions or providing helpful responses. To build a helpful assistant, GPT needs a stage called "Supervised Fine-Tuning." Here's how it works:

• GPT is given high-quality examples of questions and answers. For instance (Question: "What is the capital of France?"), (Answer: "The capital of France is Paris.")

• The model processes these pairs of questions and answers and learns to generate the correct response. How? Through algorithms like backpropagation (backward propagation of errors), the model adjusts its parameters to reduce the error in its responses. For example, if the model initially responds with just "Paris," it learns that a more complete response like "The capital of France is Paris" is preferred.

By adjusting its parameters based on these questions and answers, the model improves its accuracy and helpfulness. It learns to recognize the question format, response patterns, and to produce complete, grammatically correct sentences.

The major mechanism behind this process is called Self-Attention:

• Self-attention: A mechanism that allows GPT to weigh the importance of each word relative to others in a sentence. This process is similar to how you might highlight or pay more attention to specific key words while reading to grasp the overall meaning.

Through this process, GPT transforms from a general text completer into an agent capable of responding to specific human queries.

3. Reward Modeling: Choosing the Best Answers

Reward Modeling helps GPT choose the best answers by reinforcing preferred responses through human feedback. It’s like a multiple-choice test, Here's how:

1. GPT generates multiple answers to a question.
2. Humans evaluate these answers and select the best ones.
3. The model assigns higher rewards to the selected best answers.
4. Over time, GPT adjusts its parameters to favor these high-reward answers, improving its ability to provide accurate and helpful responses.

4. Reinforcement Learning: Getting Even Better

In the previous stage, we learned how Reward Modeling identifies and assigns rewards to preferred answers. In this stage, Reinforcement Learning uses those rewards in a continuous feedback loop to refine the model's ability to generate high-quality responses over time. Here's how it works:

• Interaction with Environment: GPT continues to receive feedback through dynamic interactions, learning from responses that lead to new questions or scenarios.
• Trial and Error: The model tries different responses and learns from the outcomes. High-reward responses are reinforced, leading to parameter adjustments to favor similar future responses. The Self-attention mechanisms help GPT learn from trial and error by focusing on which parts of previous interactions were successful in generating rewarding outcomes.
• Maximizing Cumulative Rewards: The goal is to maximize total rewards over many interactions. The model continually improves by learning which types of responses yield the best outcomes.

I hope this step-by-step guide has helped you understand GPT better. Feel free to ask questions or suggest additions in the comments below.

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