How does ChatGPT work?
With ChatGPT by Open AI revolutionizing the internet with its ability to provide coherent, human-like responses to different prompts, most tech enthusiasts are all ears to find the answer.
The intricate details of the inter technicalities powering this generative AI tool remain unpublished. Still, we can figure out the principle frameworks of the machine learning technologies used to create a large language model for the ChatGPT AI chatbot.
It must take a lot to complete the vast language processing tasks.
So, in today’s post, I discuss the outer and inner workings of the GPT model backing up the ChatGPT AI technology.
How Does ChatGPT Actually Work?
ChatGPT and Google have similar functions. These tools can interact with users and return text results for their search queries.
In Google’s case, it provides a series of website links related to the search query. It can also dig up related images, videos, and other content.
Similarly, you can find answers to any query using ChatGPT, albeit in a human-like tonality and comprehension. Unlike Google, ChatGPT provides immediate solutions or performs specific tasks instead of pulling out links.
None of these tools dig up answers from the internet right when you enter a keyword or a prompt. Instead, these tools function in two phases:
- Data gathering (Google) or Pre-training phase (ChatGPT)
- User interaction (Google) or inference phase (ChatGPT)
When a user enters a prompt in the designated field of ChatGPT, it tries to understand the context of the input text and produce a text response in human language. It analyzes a large dataset and reasonably predicts what one may expect in response to what they have just written.
Check out the following YouTube video to quickly understand the way ChatGPT works:
How Does ChatGPT Generate a Human-Like Response?
Like humans, the ChatGPT AI bot can answer a question, write programming codes, understand follow-up questions, and admit its mistakes. It can also deem a request inappropriate and reject it.
The magic of generating human-like responses lies in the fundamentals of machine learning and neural networks. As you may know, a neural network is an AI model that imitates the functions of the human brain with vast network data and computing power.
Once you enter a prompt in ChatGPT, it identifies the key phrases and themes to generate a response. Interestingly, the AI tool doesn’t read the prompt’s text.
Instead, it uses a self-attention mechanism to process the entire text and put a weighted score to every word in the sequence. Based on this score, it understands the context and semantics of the user queries.
Once understood, it generates a word-by-word response in the autoregressive process. Then, again, it uses a mix of algorithms to determine the word rankings before arranging them logically and coherently to create a natural language response.
Technically speaking, the ChatGPT AI tool can generate factually and grammatically correct responses and maintain a human-like conversation by analyzing the semantics and understanding human language patterns with the help of Large Language Model (LLM) datasets.
How Does ChatGPT Work Technically?
Technically, ChatGPT relies on Large Language Models (LLM) called GPT-3 and GPT-4, which were developed by Open AI. In fact, the GPT in ChatGPT comes from the names of the language models.
GPT stands for Generative Pre-trained Transformer, while the numerical digit refers to the version number. The GPT-4 is only available for ChatGPT Plus users, while the GPT-3.5 powers the free-to-use interface.
These LLMs were given a few parameters and large datasets to digest so they could understand the relationship between words within a text sequence or paragraph. Then, these data power up a deep learning process that can predict the next word in a sentence like magic.
1. Pre-Training the AI Chatbot
Large Language Models (LLMs) require a lot of training. While the GPT-3.5 wasn’t trained from the ground up, it took all the resources from the previous models and improved it further. Likewise, the GPT-4 model took it even further, refining the entire process and making it more capable.
Open AI pre-trained GPT-3 with roughly 45 terabytes of text data amounting to around 500 billion tokens. Due to these extensively large datasets, ChatGPT can respond with human-like coherence.
These tokens consist of a vast amount of human knowledge available digitally. It includes web content, forum answers, books, articles, and research papers on various topics, genres, and purposes.
The multi-layered algorithm was trained using an unsupervised machine learning technique, meaning that it learned to predict the next word in a sequence without any supervised instructions.
2. Deep Learning Neural Network
Open AI scrapped almost the entire web to create a deep learning neural network – a complex algorithm modeled after the human brain. The underlying machine-learning technique in the network is called transformer architecture.
The transformer architecture uses a weighting technique to determine each word’s importance in a text sequence and make predictions. The architecture has many sub-layers within a layer.
Among these, the most important artificial intelligence layers are:
- Self-attention layer. It is the process of scoring each word in a given sequence.
- Feedforward layer. It is where the AI chatbot transforms the input data into a non-linear sequence to better understand the context.
As part of the training, numerous sequences of words were given to predict the next one before comparing the predicted word to the actual and refining parameters.
With iterative refinements, GPT-3.5 and GPT-4 models have become powerful enough to perform various language processing tasks, including accurate language translation.
3. Reward Model for Data Comparison
While ChatGPT fundamentally works by predicting the next word in a sequence, it is not merely an intelligent keyboard. The functionality of ChatGPT goes way beyond that.
ChatGPT is a generative AI tool that can perform specific tasks related to language processing. For example, some popular applications of ChatGPT require it to maintain a conversation and manage the flow of interactive dialogues.
So to refine ChatGPT’s response with such complex parameters, Open AI optimized it for dialogue with a technique called RLHF ( Reinforcement Learning with Human Feedback).
In this process, human feedback was needed to create a reward model with two or more comparison data. The reward model comprised different similar response clusters to teach the AI about choosing the most refined response.
Coupled with its ability to remember previous user interactions, the RLHF reward model helps the bot maintain a natural conversation. It takes the dialogue management technique from natural language processing technologies.
4. Continuous Learning and Analyses
The extensive language model powering ChatGPT continues to be trained with new datasets to refine its response to changed contexts.
It learns from the different prompts input by its users while scouring the web for the latest information. This is when the unsupervised machine-learning technique becomes a game-changer.
The supervised learning technique also works, with humans capable of training the AI with new datasets. It is called “fine-tuning,” whereby the neural network gets acquainted with a specific data set in a particular sector.
For example, you can train the AI with text data about customer service responses to generate quick and helpful solutions to your customers’ issues. It is why developers can use the ChatGPT API in creating and training a chatbot for their unique application needs.
How Unique Are ChatGPT Responses?
ChatGPT responds uniquely to each user, even if it is an identical or similar prompt. While the answered information might be similar, the chatbot generates a unique response to each prompt in real-time.
Thanks to its advanced modeling and processing power, the AI tool generates responses based on the following:
- Contextual Information: The details of a prompt and the previous interactions set the context for ChatGPT to generate human-like text.
- Wording and Tonality: The GPT 3 model and its later version can analyze a human language’s semantics and other nuances to generate a personalized answer.
- Training Data: The vast amount of data used to train the large language model also influences the response with all its biases, inaccuracies, and other quarks.
Unlike uniqueness, the quality of a ChatGPT response depends on the quality and clarity of your prompt inputs. Therefore, you must use a natural tone to prompt the AI chatbot and get a helpful answer.
Check out the following video on YouTube discussing a few ChatGPT hacks to improve its response:
Frequently Asked Questions
Looking for quick answers to some pressing questions about Open AI’s ChatGPT? You can find them below:
Where Does ChatGPT Get Its Data?
ChatGPT was trained with a massive amount of text data. These data were sourced from web scraping, expert knowledge databases, user-generated content on social media, human feedback and interaction, open data source, etc.
Open AI actively updates its language modeling database to account for changes in human interactions.
What Is Google’s Response to ChatGPT?
In response to ChatGPT potentially damaging Google’s control on the internet, the search engine behemoth has introduced Google Bard.
Powered by its unique Language Model for Dialogue Applications (LamDA), Google Bard can work like a generative language engine similar to ChatGPT. However, Google Bard remains a few steps behind ChatGPT.
Wrapping Up
Since its release, ChatGPT has gone viral with its incredible ability to generate natural responses to various prompts and queries. It only understands and generates text data but with human-like sensitivity and intelligence.
With continuous refinements and improvements in an unsupervised learning model, ChatGPT continues to learn to perform new tasks. Different ChatGPT-backed chatbots are also being released with various unique capabilities.
Despite such a dive, I have only touched upon ChatGPT’s inner workings at a rudimentary level. I hope this will prove helpful in implementing this revolutionary AI technology for whatever purpose you may have.
