Here’s a technical deep dive into how GPT (Generative Pre-trained Transformer) works, covering its architecture, training, tokenization, and inference pipeline. This is aimed at readers with some background in machine learning, deep learning, or NLP.

Technical Deep Dive: How GPT Works

1. Transformer Architecture: The Foundation

GPT is built on the Transformer architecture, specifically using a decoder-only variant.

Core Components:

• Input Embeddings: Convert input tokens (words or subwords) into dense vectors.
• Positional Encodings: Added to embeddings to encode the position of each token (since Transformers don’t inherently understand sequence).
• Transformer Blocks: Stacked layers consisting of:
  • Multi-head self-attention mechanism
  • Layer normalization
  • Feedforward neural networks
  • Residual (skip) connections

Each Transformer block enables the model to attend to different parts of the input sequence to understand context and relationships between words.

2. Self-Attention Mechanism

Why It Matters:

Self-attention allows the model to weigh the relevance of each word in a sequence relative to every other word.

Computation:

For each token:

• Compute Query (Q), Key (K), and Value (V) vectors using learned linear projections.
• Compute attention weights:

Attention(Q,K,V)=softmax(QKTdk)V\text{Attention}(Q, K, V) = \text{softmax} \left( \frac{QK^T}{\sqrt{d_k}} \right) VAttention(Q,K,V)=softmax(dk​​QKT​)V

• d_k is the dimensionality of the key vectors, used to scale the dot product.

Masking:

GPT uses causal (autoregressive) masking, ensuring the model can only attend to past tokens when predicting the next one.

3. Training: Pretraining Phase

GPT undergoes two phases:

A. Pretraining (Self-supervised Learning)

• Objective: Predict the next token given previous ones (language modeling).
• Loss Function: Cross-entropy loss over the vocabulary:

L=−∑tlog⁡P(xt∣x<t)L = – \sum_{t} \log P(x_t | x_{<t})L=−t∑​logP(xt​∣x<t​)

• Data: Trained on vast corpora like Common Crawl, books, web text, Wikipedia, etc.
• Optimization: Adam or AdamW optimizer with learning rate warm-up and decay schedules.

Related: The Transformative Impact of AI on Education and Business

B. Fine-tuning or Instruction Tuning

• GPT-3 and GPT-4 models use Reinforcement Learning from Human Feedback (RLHF):
  1. A supervised fine-tuning step using curated human demonstrations.
  2. A reward model scores outputs.
  3. Proximal Policy Optimization (PPO) fine-tunes the model to generate better responses.

4. Tokenization: Byte-Pair Encoding (BPE)

GPT does not use words as basic units but tokens created using Byte-Pair Encoding (BPE):

• Splits text into subwords or characters based on frequency.
• Example: “unhappiness” → [“un”, “happiness”] or [“un”, “hap”, “pi”, “ness”].

Advantages:

• Handles out-of-vocabulary words.
• Balances between character- and word-level tokenization.
• Reduces vocabulary size to manageable levels (GPT-3 uses 50,257 tokens).

5. Inference: Generating Text

When given a prompt, GPT performs autoregressive generation:

1. Encode the input using the trained model.
2. Predict the probability distribution of the next token.
3. Sample or select the next token (via greedy, beam search, top-k, or nucleus sampling).
4. Repeat the process with the new token appended to the input.

This is done until a stop condition is met (e.g., max length or special token).

Sampling Techniques:

• Greedy Search: Always pick the most probable next token.
• Top-k Sampling: Randomly sample from the top k most likely tokens.
• Top-p (Nucleus) Sampling: Sample from the smallest set of tokens whose cumulative probability exceeds p (e.g., 0.9).

6. Model Parameters and Scaling

• GPT models are scalable — the architecture remains constant while depth, width, and training data scale up.
• GPT-2: 1.5 billion parameters.
• GPT-3: 175 billion parameters.
• GPT-4: Estimated to be over 500 billion parameters (exact numbers not confirmed).
• Larger models = better performance, but also higher compute and inference costs.

Scaling Laws:

Research shows that model performance improves predictably with increases in:

• Number of parameters
• Training data
• Compute power

7. Limitations and Safeguards

While GPT is powerful, it has known limitations:

• Hallucinations: Confidently generating false or unverifiable information.
• Bias: Reflecting harmful stereotypes in training data.
• Lack of understanding: GPT models do not have true reasoning, common sense, or real-world grounding.
• Token limits: Larger models are restricted by context window sizes (e.g., 8K, 32K, or 128K tokens).

Safeguards:

• OpenAI implements content filters, alignment training (via RLHF), and usage policies to mitigate harmful output.

8. Applications

GPT models are used for:

• Conversational agents (e.g., ChatGPT)
• Code generation (e.g., GitHub Copilot)
• Summarization and translation
• Legal, medical, and academic research assistance
• Creative writing, ideation, and design

Summary

Component	Description
Architecture	Decoder-only Transformer
Training	Predict next token via autoregressive language modeling
Data	Massive internet-scale datasets
Tokenization	Byte Pair Encoding (BPE)
Generation	Autoregressive; sampling strategies
Fine-tuning	RLHF for alignment and safety
Applications	Broad range: writing, coding, analysis, assistance