What is GPT (Generative Pretrained Transformer)?

What is GPT?

GPT stands for Generative Pretrained TraCertificate Program in AI Business Strategynsformer, a type of artificial intelligence model designed to understand and generate human-like text. It’s the backbone of powerful AI applications like ChatGPT, revolutionizing the way we interact with machines.

Breakdown of the Term: Generative Pretrained Transformer

• Generative – GPT is capable of creating coherent and contextually relevant text, mimicking human-like responses across various topics.

• Pretrained – Before fine-tuning for specific tasks, GPT undergoes extensive training on vast datasets containing diverse text sources, enabling it to grasp grammar, facts, and reasoning patterns.

• Transformer – At its core, GPT uses a neural network architecture known as a Transformer, which leverages attention mechanisms to process language efficiently, ensuring context-aware and meaningful text generation.

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Evolution of GPT Models

1. GPT-1

Release: 2018

Key Features: 

• GPT-1 was the inaugural model that introduced the concept of using a transformer architecture for generating coherent text.
• This version served primarily as a proof of concept, demonstrating that a generative model could be effectively pre-trained on a large corpus of text and then fine-tuned for specific downstream tasks.
•  With 117 million parameters, it showcased the potential of unsupervised learning in understanding and generating human-like language.
• The model learned contextual relations between words and phrases, displaying fundamental language generation capabilities.

2. GPT-2 

Release: 2019

Key Features: 

• GPT-2 marked a significant leap in scope and scale with 1.5 billion parameters, highlighting the impact of model size on performance.
• The model generated notably fluent and contextually rich text, capable of producing coherent responses to prompts.
• DeepAI opted for a phased release due to concerns over potential misuse, initially publishing a smaller model before gradually releasing the full version.
• Its capabilities included zero-shot and few-shot learning, allowing it to perform various tasks without extensive fine-tuning, such as translation, summarization, and question answering.

3. GPT-3

Release: 2020

Key Features: 

• GPT-3 represented a monumental leap in model size, featuring 175 billion parameters, which dramatically enhanced its language understanding and generation capabilities.
• This version showcased remarkable versatility across diverse applications, performing tasks as varied as creative writing, programming assistance, and conversational agents with minimal instructions, often achieving state-of-the-art results.
• The introduction of the “few-shot” learning paradigm allowed GPT-3 to adapt to new tasks with only a few examples, significantly reducing the necessity for task-specific fine-tuning.
• Its contextual understanding and coherence surpassed previous models, making it a powerful tool for developers in building AI-driven applications.

4. GPT-4

Release: 2023

Key Features: 

• GPT-4 built on the strengths of its predecessor with improvements in reasoning, context management, and understanding nuanced instructions.
• While specific parameter counts were not disclosed, it is believed to be even larger and better than GPT-3, featuring enhancements in architectural techniques.
• This model exhibited better contextual understanding, allowing for more accurate and reliable text generation while minimizing instances of producing misleading or factually incorrect information.
• Enhanced safety and alignment measures were implemented to mitigate misuse, reflecting a broader focus on ethical AI development.
• GPT-4’s capabilities extended to multimodal tasks, meaning it could process not just text but also images, thereby broadening the horizon of potential applications in various fields.

Also read: How to create custom GPTs?

Understanding the GPT Architecture

1. Tokenization & Embeddings

• GPT breaks down text into smaller units called tokens (words, subwords, or characters).
• These tokens are then converted into dense numerical representations, known as embeddings, which help the model understand relationships between words.

2. Multi-Head Self-Attention Mechanism
   • This is the core of the Transformer model. Instead of processing words one by one (like RNNs), GPT considers all words in a sequence simultaneously.
   • It uses self-attention to determine the importance of each word concerning others, capturing long-range dependencies in text.

3. Feed-Forward Neural Networks
   • Each Transformer block has a fully connected neural network that refines the output from the attention mechanism, enhancing contextual understanding.

4. Positional Encoding

• Since Transformers don’t process text sequentially like traditional models, positional encodings are added to tokens to retain the order of words in a sentence.

5. Layer Normalization & Residual Connections
   • To stabilize training and prevent information loss, layer normalization and residual connections are used, helping the model learn effectively.

6. Decoder-Only Architecture
   • Unlike BERT, which has both an encoder and a decoder, GPT is a decoder-only model. It predicts the next token in a sequence using previously generated words, making it ideal for text completion and generation tasks.

7. Pretraining & Fine-Tuning
   • GPT is first pretrained on massive datasets using unsupervised learning.
   • It is then fine-tuned on specific tasks (e.g., chatbot conversations, summarization, or code generation) to improve performance.

How does GPT (Generative Pre-trained Transformer) Operate?

1. Input Preparation

• Tokenization: The input text (e.g., a sentence or a prompt) is first tokenized into manageable units. GPT typically uses a subword tokenization method like Byte Pair Encoding (BPE), which breaks down unfamiliar words into more familiar subword components.

• Encoding: Each token is mapped to a corresponding embedding vector in an embedding matrix. This vector represents the token in a continuous space, allowing the model to make calculations.

2. Adding Positional Encodings

Since transformers do not have a built-in mechanism to understand the order of words (unlike recurrent neural networks), positional encodings are added to each token embedding. Positional encodings provide information about the position of each token in the sequence, incorporating sequential order into the model.

Processing Through Transformer Decoder Layers

• Self-Attention Mechanism: In each layer, the self-attention mechanism allows the model to focus on different parts of the input sequence. 

• Calculating Attention Scores: For each token in the input, the model computes three vectors: query (Q), key (K), and value (V). These vectors are derived from the input embeddings through learned linear transformations.

• The attention scores are computed by taking the dot product of the queries and keys, scaled by the square root of the dimensionality, followed by a softmax operation to produce attention weights. This determines how much attention each token should pay to every other token in the sequence.

• Weighted Sum: The output for each token is computed as a weighted sum of the value vectors, based on the calculated attention weights.

3. Multi-Head Attention

Instead of using a single set of attention weights, GPT uses multiple “heads.” Each head learns different attention patterns. The outputs from all heads are concatenated and transformed to produce the final output of the attention mechanism for that layer.

Feed-Forward Neural Networks

After the attention calculation, the output is passed through a feed-forward neural network (FFN), which applies a non-linear transformation separately to each position in the sequence.

Residual Connections and Layer Normalization

Both the attention output and the FFN output are added to their respective inputs through residual connections. Layer normalization is then applied to stabilize and speed up training.

This process repeats for each layer in the transformer decoder.

4. Final Output Computation

After passing through all transformer decoder layers, the final output vectors are obtained. Each vector corresponds to a token in the input.

These output vectors are then transformed through a final linear layer that projects them onto the vocabulary size, producing logits for every token in the vocabulary.

5. Generating Predictions

To produce predictions, GPT uses a softmax function to convert the logits into probabilities for each token in the vocabulary. The output now indicates how likely each token is to follow the input sequence.

6. Token Sampling

The model selects the next token based on the probabilities. Various sampling methods can be used:

• Greedy Sampling: Choosing the token with the highest probability.
• Top-k Sampling: Selecting from the top-k probable tokens.
• Top-p Sampling (nucleus sampling): Selecting from the smallest set of tokens whose cumulative probability exceeds a certain threshold (p).

The chosen token is then added to the input sequence.

7. Iterative Generation

Steps 3 to 6 are repeated iteratively. The model takes the newly generated token, appends it to the input sequence, and processes the updated sequence again to predict the next token. This continues until a stopping criterion is met (e.g., reaching a specified length, hitting a special end-of-sequence token, etc.).

Applications of GPT

1. Conversational AI & Chatbots

• Powers virtual assistants like ChatGPT, handling customer queries, automating responses, and enhancing user interactions.
• Used in customer service, technical support, and AI-driven help desks to provide instant, contextually relevant responses.

2. Content Creation & Copywriting

• Assists in writing articles, blogs, marketing copies, and creative stories with human-like fluency.
• Used by businesses, content creators, and digital marketers for generating SEO-friendly content and automating social media posts.

3. Code Generation & Software Development

• GPT models like Codex (a variant of GPT-3) assist developers by generating, debugging, and optimizing code.
• Supports multiple programming languages, enabling faster software development and AI-assisted coding.

4. Personalized Education & Tutoring

• Enhances adaptive learning platforms, offering personalized study plans, AI-driven tutoring, and instant explanations.
• Helps students with essay writing, language translation, and problem-solving in subjects like math and science.

5. Research & Data Analysis

• Assists in summarizing research papers, generating insights from large datasets, and drafting technical documents.
• Used in industries like finance, healthcare, and law for analyzing trends and automating reports.

Also Read: How to use ChatGPT?

Strengths and Limitations of GPT