Understanding How ChatGPT Works: A Step-by-Step Guide

ChatGPT, developed by OpenAI, is a sophisticated language model that can generate human-like responses to various queries. But how exactly does it work? In this post, I’ll walk you through the core components of ChatGPT’s functionality, using examples, tables, and figures to make the process easier to understand.

Reference: How ChatGPT Works: A Mini Review

1. Input Embedding: Turning Words into Vectors

When ChatGPT processes a sentence, the first step is tokenization, where the input is broken down into individual words, or tokens. Each token is then transformed into a numerical vector—a representation in high-dimensional space that captures the meaning of the word.

For example, if a doctor inputs the query, “Write a strategy for treating otitis in a young adult”, ChatGPT tokenizes it and assigns numerical values to each token, known as word embeddings.

Tokenization and Word Embeddings Table:

Tokenized Words	Word Embedding (Vector Representation)
Write	0.15
Strategy	0.40
For	0.12
Treating	0.80
Otitis	0.92
In	0.30
A	0.05
Young	0.65
Adult	0.55

2. The Encoder: Understanding Context

The encoder is a critical component of ChatGPT, tasked with processing the numerical vectors generated during input embedding. It captures relationships between words and learns the meaning of the sentence as a whole. In models like GPT-4, this step uses self-attention, allowing the model to focus on the entire input sequence simultaneously.

In this case, the encoder understands that “otitis” refers to an ear infection and “young adult” refers to an age group, helping the model frame appropriate responses.

3. The Decoder: Generating Responses

After encoding, the decoder takes over. The decoder generates possible responses by converting the encoded information back into output vectors, which are then turned into words.

For example, the decoder might suggest several treatment strategies for otitis, such as antibiotics or pain relievers.

Decoder Output Table:

Decoder Output	Suggested Treatment for Otitis
0.75	Antibiotics
0.65	Pain Relievers
0.55	Ear Drops
0.40	Follow-Up Monitoring

4. Attention Mechanism: Focusing on What Matters

One of the standout features of ChatGPT is the attention mechanism. As the decoder generates responses, it selectively focuses on the most relevant parts of the input. For instance, when suggesting a treatment for otitis, ChatGPT might focus more on “otitis” and “infection” than on “young adult”.

5. Output Projection: Generating the Final Response

Once the decoder has generated potential responses, these are passed through a layer known as softmax, which produces a probability distribution. This step helps determine which solution is most appropriate based on the context of the query.

For example, ChatGPT might calculate that antibiotics are the most likely appropriate treatment for otitis, based on the input and encoded information.

Additional Features of GPT-4

• Internet Connectivity: GPT-4 can access the internet to ensure up-to-date information is available, making responses more accurate and timely.
• Plugins for Enhanced Functionality: Plugins can be used to extend GPT-4’s capabilities. For instance, it can generate images, translate text, and more.
• Multimodal Abilities: GPT-4 can process inputs in various formats, such as text and images, and generate multimodal outputs based on the data.

Pitfalls of Large Language Models (LLMs)

Pitfall	Description
Lack of Common Sense	LLMs, including GPT-4, may generate responses that seem coherent but lack real-world common sense.
Hallucination of Facts	LLMs sometimes “invent” facts that sound plausible but are not grounded in reality. This can lead to misinformation.
Context Window Limitations	LLMs have limited memory in long conversations. When the conversation exceeds the context window, the model may forget earlier parts.
Privacy Concerns	Inputting sensitive data can pose privacy risks. Users should be cautious not to input identifiable patient information.

Understanding how ChatGPT works—from tokenization to generating final responses—can help you see the sophistication behind its seemingly simple outputs. While LLMs like GPT-4 have vast potential, especially in fields like healthcare, it’s important to be aware of their limitations and use them responsibly.

Note : this blog post so far is from this article:

Briganti, G. How ChatGPT works: a mini review. Eur Arch Otorhinolaryngol 281, 1565–1569 (2024). https://doi.org/10.1007/s00405-023-08337-7

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Understanding How ChatGPT Works: A Detailed Breakdown

To understand how ChatGPT works, we need to take a deeper look at how it updates its parameters during the learning process and how this is different from traditional RNNs (Recurrent Neural Networks) and simpler neural networks.

1. Traditional Neural Networks & RNNs: Weight Updates

In traditional neural networks, and even in RNNs, the core of the learning process lies in weight updates. Here’s how it generally works:

• Forward Pass: Data (e.g., text, images) is passed through layers of the neural network, where each neuron in a layer takes input from the previous layer and multiplies it by a weight. The weighted sum is passed through an activation function to introduce non-linearity (e.g., a ReLU or sigmoid function).
• Backward Pass (Backpropagation): After the network predicts an output, an error is calculated by comparing the predicted output to the actual output. The network then adjusts its weights in reverse order (from output to input), using backpropagation to minimize the error. The weight adjustments are based on gradients computed from the error using gradient descent.
• RNNs: In RNNs, the network processes sequences by maintaining hidden states across time steps. This allows it to “remember” previous inputs. However, RNNs struggle with long-term dependencies due to issues like vanishing gradients, which occur during backpropagation through many layers (or time steps). This is where transformers like GPT shine, as they don’t rely on this sequential processing.

Key Differences Between RNNs and Transformers (ChatGPT)

Feature	RNNs	Transformers (ChatGPT)
How Sequences are Processed	Sequentially (step by step)	All at once (parallel processing)
Handling Long-Range Dependencies	Struggles due to vanishing gradients	Handles well with self-attention
Training Efficiency	Slower, less efficient	Faster, highly efficient
Context Length	Limited	Handles very long texts (up to 25,000 words)

2. How ChatGPT (Transformer) Works: Weight Updates

In ChatGPT, which uses a transformer architecture, the mechanism for updating weights is fundamentally similar but operates over a more sophisticated architecture.

Key Differences in Transformers (ChatGPT)

• Self-Attention Mechanism: Instead of processing sequences step by step (like RNNs), transformers use self-attention to compare every word in a sentence with every other word at once. This allows the model to better capture relationships between distant words in a sentence.
• Multi-Headed Attention: Transformers use multiple attention heads that look at the input data from different perspectives. Each attention head updates its own set of weights, learning different relationships within the sentence.
• Layered Network: After the self-attention step, the information is passed through traditional feedforward neural networks (fully connected layers) within each layer of the transformer. These networks apply more transformations to the data and further adjust weights during training.
• Positional Encoding: Transformers don’t process sequences step by step like RNNs, so they use positional encodings to indicate the order of the words. These encodings are combined with the input embeddings to give the model information about the position of each word in the sentence.

3. How GPT-4 is Different and Better

GPT-4 has trillions of parameters, far exceeding GPT-3’s 175 billion. These parameters include all the weights in the attention layers, feedforward layers, and output layers, allowing GPT-4 to capture more complex patterns in language.

Why GPT-4 is Better

• Larger Neural Network: GPT-4’s immense neural network size enables it to handle more complexity and nuance, capturing subtler language patterns.
• Better Gradient Flow: Improvements in gradient handling make GPT-4 more effective at backpropagation, reducing vanishing gradient issues.
• Reinforcement Learning: GPT-4 benefits from Reinforcement Learning from Human Feedback (RLHF), improving fine-tuning based on user feedback.

4. Step-by-Step Mechanism in ChatGPT

Step	Description
Step 1: Tokenization and Embedding	The input is split into tokens (words) and converted into numerical embeddings representing their meaning.
Step 2: Self-Attention Mechanism	Tokens are compared to understand relationships using self-attention.
Step 3: Multi-Head Attention	Multiple attention heads process different aspects of the input simultaneously.
Step 4: Feedforward Neural Networks	The output of attention layers is passed through feedforward neural networks.
Step 5: Output Projection and Softmax	The final output is generated using softmax to predict the next token.

Why GPT-4 is the Best Version (2024)

• Trillions of Parameters: GPT-4’s immense size allows it to handle much more complexity and nuance.
• More Efficient Training: GPT-4 has been fine-tuned with better gradient handling techniques, making it more stable during training and reducing errors like hallucinations.
• Larger Context Window: GPT-4 can process up to 25,000 words at a time, improving its ability to maintain coherence in long conversations.
• Improved Attention Mechanisms: With more attention heads and layers, GPT-4 can focus on multiple aspects of language simultaneously.
• Multimodal Capabilities: GPT-4 can process both text and images, opening new possibilities for AI applications.

Conclusion

ChatGPT’s strength lies in its transformer-based neural network architecture, which uses self-attention mechanisms, multi-headed attention, and massive amounts of parameters to generate coherent, contextually relevant responses. With GPT-4, these capabilities have been scaled to trillions of parameters, enabling more accurate language generation, better problem-solving, and the ability to handle longer texts.