Understanding How ChatGPT Processes Input: A Step-by-Step Guide

Table of Contents

• Introduction
• Step 1: Input Tokenization
• Step 2: Token Embedding
• Step 3: Positional Encoding
• Step 4: Input to the Transformer
• Step 5: Multi-Head Self-Attention Mechanism
• Step 6: Add & Normalize
• Step 7: Position-wise Feedforward Network
• Step 8: Stacking Multiple Transformer Layers
• Step 9: Generating Output Probabilities
• Step 10: Predicting the Next Token
• Step 11: Decoding the Output Token
• Step 12: Iterative Generation
• Step 13: Summary of Neural Network Components
• Conclusion

Introduction

ChatGPT is a language model based on the Transformer architecture. It generates responses by processing input text through several neural network layers. By understanding each step, we can appreciate how ChatGPT generates coherent and contextually appropriate replies.

Step 1: Input Tokenization

What Happens?

The input text is broken down into smaller units called tokens. ChatGPT uses a tokenizer based on Byte Pair Encoding (BPE).

Neural Network Involvement:

No — Tokenization is a preprocessing step, not part of the neural network.

Example:

Input Text: “Hi”

Tokenization Process:

Input Text: "Hi"
       ↓
Tokenization
       ↓
Token IDs: [2]
        

Step 2: Token Embedding

What Happens?

Each token ID is mapped to a token embedding vector using an embedding matrix. The embedding represents the semantic meaning of the token.

Neural Network Involvement:

Yes — This is part of the neural network as the Embedding Layer.
Here, the embedding matrix is learned during training, which means during the backpropagation process, the embedding matrix is updated based on the gradients calculated for errors in predictions. Every time the model processes a token, the embedding matrix adjusts its values to better represent the relationships between tokens and their contexts.

Mathematical Representation:

The embedding matrix has a size of , where is the vocabulary size and is the model’s hidden dimension (e.g., 768).

For Token ID 2:

Example:

Assuming :

Embedding Vector:

Token ID	Embedding Vector ()
2	[0.1, 0.2, 0.3, 0.4]

Step 3: Positional Encoding

What Happens?

Positional encodings are added to embeddings to give the model information about the position of tokens in the sequence. This is done using sine and cosine functions.

Neural Network Involvement:

No — Positional encoding is a mathematical function applied to embeddings and is not learned. However, it is crucial because without this, the Transformer model would have no understanding of the order of the tokens.

Mathematical Representation:

For position , for each dimension :

Positional Encoding Vector:

Adding Positional Encoding:

Dimension
0	0.1	0.8415	0.9415
1	0.2	0.5403	0.7403
2	0.3	0.00999983	0.30999983
3	0.4	0.99995	1.39995

Step 4: Input to the Transformer

What Happens?

The positionally encoded embeddings are passed as input to the Transformer model.

Neural Network Involvement:

Yes — The Transformer model is a neural network with multiple layers. This step feeds the embeddings into the first layer of the Transformer architecture.

Input Vector:

Positionally Encoded Embedding (\( \mathbf{z}_1 \))
       ↓
Input to Transformer
        

Step 5: Multi-Head Self-Attention Mechanism

What Happens?

The Transformer applies the self-attention mechanism to the input embeddings. Self-attention allows the model to weigh the importance of different tokens relative to each other. This is a crucial part of the neural network, responsible for learning relationships between tokens.

Neural Network Involvement:

Yes — Self-attention is a neural network mechanism where the model learns weights (importance) for each token relative to others. The learned weights , , and are updated during backpropagation. Each token is transformed into three different representations: queries (), keys (), and values (), and these representations are then used to compute attention scores.

Components:

• Weights: (query), (key), (value), each of size .

Mathematical Operations:

1. Compute Queries, Keys, and Values:

   , , 

2. Compute Attention Scores:

3. Compute Attention Weights:

    (since there’s only one token in this example).

4. Compute Attention Output:

Example Simplification:

Assume (identity matrix).

Attention Score:

Component	Value
	[0.9415, 0.7403, 0.30999983, 1.39995]
	Same as
	Same as
Score	1.7452
Attention Weight
Output	Same as

Step 6: Add & Normalize

What Happens?

A residual connection adds the original input to the output of the self-attention layer. Layer normalization is then applied to stabilize training. This step ensures that the output remains within a manageable range for future layers.

Neural Network Involvement:

Yes — This is part of the neural network. The residual connection ensures that gradients flow through the network effectively, preventing the vanishing gradient problem. Layer normalization includes learnable parameters and , which are updated during training.

Mathematical Representation:

1. Residual Connection:
2. Layer Normalization:

Example Simplification:

Component	Value
	[1.8830, 1.4806, 0.61999966, 2.7999]
Mean	1.6959
Standard Deviation	0.772
	[0.242, -0.279, -1.396, 1.433]

Step 7: Position-wise Feedforward Network

What Happens?

The feedforward network applies a two-layer neural network to each position independently. The main purpose of this is to allow non-linearity and enable more complex transformations of the data.

Neural Network Involvement:

Yes — This is part of the neural network. The two linear layers (with learnable weights , ) and a non-linear activation function (like ReLU) are key components of a neural network. The weights are updated through backpropagation during training.

Mathematical Representation:

1. First Linear Transformation and ReLU Activation:

2. Second Linear Transformation:

3. Residual Connection and Layer Normalization:

Example Simplification:

Assume and are identity matrices, and .

Compute :

Input: \( \mathbf{n}_1 \)
       ↓
First Linear Transformation + ReLU
       ↓
Second Linear Transformation
       ↓
Add Residual Connection (\( \mathbf{n}_1 + \mathbf{f}_1 \))
       ↓
Layer Normalization
       ↓
Output: \( \mathbf{o}_1 \)
        

Step 8: Stacking Multiple Transformer Layers

What Happens?

The output is passed through multiple Transformer layers. This step repeats the self-attention mechanism and feedforward network multiple times (e.g., 12 layers in GPT models). Each layer has its own learnable parameters and allows the model to capture more abstract relationships in the data.

Neural Network Involvement:

Yes — Each Transformer layer is a neural network with its own set of weights. The process of stacking layers allows the model to build hierarchical representations of the input. The weights in each layer are updated independently during training.

Input: \( \mathbf{o}_1^{(0)} = \mathbf{o}_1 \)
       ↓
Layer 1: \( \mathbf{o}_1^{(1)} \)
       ↓
Layer 2: \( \mathbf{o}_1^{(2)} \)
       ↓
...
       ↓
Layer N: \( \mathbf{o}_1^{(N)} \)
        

Step 9: Generating Output Probabilities

What Happens?

The final output is projected to the vocabulary space to get logits. Logits are converted to probabilities using the softmax function. This step helps the model predict the most likely next token.

Neural Network Involvement:

Yes — The output layer is a neural network layer that transforms the hidden representation into logits. The projection matrix and the bias are learned during training and are updated via backpropagation.

Mathematical Representation:

1. Compute Logits:

2. Compute Probabilities:

Example Simplification:

Assume vocabulary size .

Logits:

Token ID	Logit		Probability
0	2.0	7.389
1	1.0	2.718
2	3.0	20.086
3	0.5	1.648

Step 10: Predicting the Next Token

What Happens?

The token with the highest probability is selected as the next token.

Neural Network Involvement:

Yes — The model uses the probabilities generated by the output layer (neural network) to select the most probable next token. This is often done by selecting the token with the highest probability (greedy decoding) or sampling based on the probability distribution (stochastic decoding).

Example:

Token ID 2 has the highest probability ().

Step 11: Decoding the Output Token

What Happens?

The predicted token ID is converted back into text using the tokenizer’s vocabulary.

Neural Network Involvement:

No — This is a post-processing step where the token ID is converted back to its corresponding word or subword in the vocabulary. This step is not part of the neural network itself but is required for outputting human-readable text.

Example:

Token ID 2 → “there”

Step 12: Iterative Generation

What Happens?

The predicted token is added to the input sequence, and the process repeats from Step 2 to generate subsequent tokens. The model continues generating tokens until a stopping condition is met, such as generating a special “end-of-sequence” token or reaching a specified token limit.

Neural Network Involvement:

Yes — The same neural network layers are reused iteratively to generate each new token. The model processes the entire sequence each time, and backpropagation occurs after the entire sequence is generated during training. The weights are continuously updated based on the errors in predicting each token.

First Iteration:
Input: [2] ("Hi")
Output Token: "there"

Second Iteration:
Input: [2, 2] ("Hi there")
Output Token: ...

And so on.
        

Step 13: Summary of Neural Network Components

Embedding Layer:

• Embedding Matrix : Maps token IDs to vectors. The embeddings are updated during training through backpropagation.

Transformer Layers:

• Multi-Head Self-Attention:
  • Weights , , are learned. During backpropagation, the gradients are calculated, and these weights are updated accordingly.
• Layer Normalization: Parameters , are updated during training to ensure stable learning.
• Feedforward Network:
  • Weights , and biases , are learned and updated during training.

Output Layer:

• Projection Matrix : Projects the hidden representations into the vocabulary space. The weights are updated during backpropagation.
• Bias : Learnable parameter, updated during training.

Conclusion

In this detailed walkthrough, we have explored how ChatGPT processes the simple input “Hi” through various steps, emphasizing the neural network components and how weights are updated during training.

Each neural network component—including the embedding layer, Transformer layers, and output layer—contains learnable parameters (weights and biases) that are adjusted during the training phase. These adjustments are made through a process called backpropagation, where the model calculates the gradients of the loss function with respect to each parameter and updates them to minimize the loss.

During inference (when the model is used to generate responses), the weights are fixed. The model uses the learned parameters to process new inputs and generate outputs based on the patterns it learned during training.

By understanding these steps and the role of neural network components, we gain insight into how ChatGPT generates responses and how the model learns from data. This highlights the complexity and sophistication of modern language models.

Note: In practice, models like ChatGPT operate with much larger dimensions (e.g., or higher) and vocabulary sizes. The computations are performed using optimized numerical libraries on specialized hardware like GPUs or TPUs to handle the intensive processing requirements.

If you have any further questions or would like additional clarifications on any part, feel free to ask!