How does ChatGPT work?
Understand the 6 stages: (1) Pre-training (2) Fine-tuning (3) Tokenization (4) Context encoding (5) Response generation (6) Iterative refinement.
Did you know?
ChatGPT does not possess true understanding or consciousness.
ChatGPT responses are generated based on patterns learned from the data it was trained on.
ChatGPT sometimes gives plausible-sounding but incorrect or nonsensical answers!
ChatGPT (Chatbot Generative Pre-trained Transformer) is based on a type of deep learning model specifically designed for natural language processing tasks.
Here's how ChatGPT works:
Pre-training
Fine-tuning
Tokenization
Context encoding
Response generation
Iterative refinement
Let's dig deeper into these.
1. Pre-training
The first step involves pre-training the model on a large dataset containing text from websites, books, articles, etc.
This helps the model learn the structure and patterns of the language, as well as facts and common knowledge up to the point when the training data was collected.
During this phase, the model learns to predict the next word in a sentence given the context of the previous words.
Example:
Consider the model is given the following sentences from its training data:
"The cat climbed up the tree."
"The dog chased the cat."
"She has a pet cat."
During pre-training, the model is trained to predict the next word in a sentence given the context of the previous words.
The model is typically exposed to a large number of sentences with one or more words masked, and it learns to predict the masked words based on the surrounding context.
This process is called "masked language modeling."
Example
The model might be given the following partially masked sentences:
"The cat climbed up the [MASK]."
"The [MASK] chased the cat."
"She has a pet [MASK]."
The model will then attempt to predict the masked words:
"The cat climbed up the tree."
"The dog chased the cat."
"She has a pet cat."
By learning from a vast amount of text, the model captures the statistical patterns & relationships between words, which helps it make accurate predictions.
It starts to understand that "climbed up" is often followed by words like "tree" or "ladder," or that "chased" is likely followed by an object (e.g., "cat" or "ball").
As the model processes more text, it becomes better at predicting the next word based on the context, eventually enabling it to generate coherent and contextually relevant responses when fine-tuned for a specific task, such as generating responses in a conversational setting.
2. Fine-tuning
After pre-training, the model is fine-tuned on a narrower dataset, which usually consists of conversation-like data.
This helps the model adapt to generating more coherent and contextually relevant responses in a conversational setting.
Let's take some examples.
Conversational AI
To create a chatbot, the model might be fine-tuned on a dataset consisting of conversation-like data, including question-answer pairs, dialogues, or customer support interactions. This fine-tuning helps the model better understand the structure and flow of conversations, making it more adept at generating contextually appropriate responses in a chatbot setting.
Sentiment Analysis
For sentiment analysis, the model could be fine-tuned on a dataset containing sentences or paragraphs labeled with their sentiment (e.g., positive, negative, or neutral). Fine-tuning in this case would involve training the model to predict the sentiment label based on the input text, refining its ability to recognize and classify emotions in text.
Text Summarization
To create a model that can generate summaries of long articles or documents, fine-tuning might be done on a dataset consisting of pairs of long-form text and their corresponding summaries. The model would learn to generate shorter, coherent summaries based on the input text while retaining the most important information.
Medical Text Analysis
To develop a model specialized in understanding medical terminology and concepts, fine-tuning could be performed on a dataset containing medical articles, research papers, or case studies. This allows the model to become more proficient in processing and generating text related to the medical domain.
3. Tokenization
When a user inputs text, the model processes it by breaking it down into smaller units called tokens.
Tokens are usually whole words or punctuation marks e.g. consider the sentence
"ChatGPT is an AI language model."
This sentence can be tokenized into the following tokens:
ChatGPT
is
an
AI
language
model
Subwords are smaller units derived from words, often used for languages where splitting text into whole words might not be the most efficient approach or for handling out-of-vocabulary words.
They are especially useful for languages with complex morphology or when words can be formed by combining smaller units e.g. German, Hungarian, Turkish, Korean, and Swahili.
Example
For example, consider the compound German word
"Eisenbahnknotenpunkthauptbahnhof"
(meaning "main railway station serving as a major junction").
A subword tokenizer might break it down into the following subwords:
Eisen
bahn
knoten
punkt
haupt
bahn
hof
In the case of English, subwords can be useful for handling rare words or words with common prefixes or suffixes.
Example
The word "unbelievable" might be tokenized into subwords as follows:
un
believe
able
Using subwords helps language models like ChatGPT to better handle rare or unseen words and improve the efficiency of text processing.
4. Context encoding
The tokenized input is then processed by a series of neural network layers called transformers.
These layers are designed to capture the contextual information within the input text, helping the model understand the relationships between words, phrases, and sentences.
Let's take some examples.
Homonyms
Consider the sentence "The bank by the river is a great spot for a picnic." In this context, "bank" refers to the side of a river. However, in the sentence "I deposited money at the bank today," "bank" refers to a financial institution. A well-trained language model can encode the context around the word "bank" to distinguish its meaning based on the surrounding words, ensuring that the generated responses are contextually appropriate.
Pronoun Resolution
Context encoding helps the model identify the correct referent for pronouns. For instance, in the sentence "Tom gave Sarah a book because she loves to read," the model can understand that "she" refers to "Sarah" and not "Tom" due to the context provided by the surrounding words.
Sentiment Analysis
Understanding the sentiment of a sentence often relies on the context in which words are used. For example, in the sentence "I am not happy with the product," the model needs to encode the context to recognize the negation "not" that changes the sentiment from positive to negative.
Idiomatic Expressions
Context encoding helps the model understand idiomatic expressions or phrases whose meanings cannot be inferred directly from the individual words. For example, in the sentence "He spilled the beans about the surprise party," the model can recognize that "spilled the beans" is an idiomatic expression meaning "revealed the secret," based on the context provided by the surrounding words.
5. Response generation
Based on the context encoding and the learned patterns from the training data, the model generates a response by predicting a sequence of tokens.
The response is then decoded into human-readable text and returned to the user.
Question-Answering
Input: "What is the capital of France?"
Generated Response: "The capital of France is Paris."
Conversation
Input: "I'm feeling really stressed about my upcoming exams."
Generated Response: "It's normal to feel stressed about exams. Try to break your study sessions into manageable chunks and give yourself breaks. Also, make sure you get enough sleep and maintain a healthy diet."
Text Summarization
Input: "ChatGPT is a language model developed by OpenAI based on the GPT architecture, which is designed for natural language processing tasks. It goes through two main phases of training: pre-training and fine-tuning. Pre-training involves learning language patterns and structures from diverse text sources, while fine-tuning adapts the model to a specific task or domain."
Generated Response: "ChatGPT is an AI language model created by OpenAI, trained in two phases: pre-training for general language understanding and fine-tuning for specific tasks or domains."
Sentiment Analysis
Input: "The movie was a visual masterpiece, but the plot was dull and uninspired."
Generated Response: "It seems like you appreciated the visual aspects of the movie but found the storyline lacking."
Recommending Activities
Input: "I'm visiting New York City for the first time. What should I do?"
Generated Response: "Some must-see attractions in New York City include visiting the Statue of Liberty, exploring Central Park, going to the top of the Empire State Building, and catching a Broadway show."
In each of these examples, ChatGPT generates a response based on the input text and its understanding of the context.
The generated responses aim to be coherent, contextually relevant, and informative, showcasing the model's ability to handle various natural language processing tasks.
6. Iterative refinement
The process of generating a response might involve multiple iterations, with the model considering different possible responses and refining its output based on the likelihood of the generated tokens.
Iterative refinement in the context of AI language models like ChatGPT refers to the process by which the model generates and refines responses through multiple iterations.
During each iteration, the model considers different possible responses and refines its output based on the likelihood of the generated tokens or the feedback it receives. This process helps improve the quality, coherence, and relevance of the generated response.
Here are some examples of iterative refinement in different situations:
Paraphrasing
Input: "Artificial intelligence has the potential to revolutionize many industries."
First Iteration: "AI can transform a variety of sectors dramatically."
Second Iteration: "Artificial intelligence has the power to significantly change numerous industries."
Writing a Haiku
Input: "Write a haiku about autumn."
First Iteration: "Autumn leaves falling,
Crisp breeze whispers through the trees,
Nature's colors change."
Second Iteration: "Leaves fall in autumn,
Gentle winds murmur softly,
Vibrant hues abound."
Generating a Book Title
Input: "Suggest a title for a science fiction novel about space exploration."
First Iteration: "Galactic Pioneers: A Voyage Through the Cosmos"
Second Iteration: "Starbound Odyssey: Explorers of the Infinite"
Elaborating on an Idea
Input: "Explain the benefits of renewable energy."
First Iteration: "Renewable energy is environmentally friendly, reduces dependence on fossil fuels, and provides a sustainable source of power."
Second Iteration: "The benefits of renewable energy include its minimal impact on the environment, decreased reliance on finite fossil fuel resources, and the provision of a long-term, sustainable energy solution."
Refining a Product Description
Input: "Describe a new smartphone with an innovative camera."
First Iteration: "Introducing the latest smartphone, equipped with a groundbreaking camera that captures stunning photos in any lighting condition, featuring advanced AI-powered editing tools to make your memories come to life."
Second Iteration: "Discover the cutting-edge smartphone with a revolutionary camera system, designed to take breathtaking photos in all lighting scenarios, and enhanced with AI-driven editing capabilities to bring your cherished moments to life."