Introduction
It's already viral and many in the tech space know and already experienced it. In this article, I will talk about ChatGPT, a natural language processing (NLP) based artificial intelligence model developed by OpenAI. ChatGPT is a variant of the popular GPT-3 language model, which has been widely recognized as a major milestone in the field of artificial intelligence (AI) and NLP (natural language processing). In this article, I will provide a detailed look at the background and technical details of ChatGPT. I will also discuss the key differences between ChatGPT and its predecessor, GPT-3.
Background of OpenAI and General AI models built by them
OpenAI is a research organization that aims to promote and advance the field of AI drastically. Since its founding in 2015, OpenAI has made significant contributions to the development of AI models, including the GPT series of language models.
The first version of GPT (Generative Pre-trained Transformer) was released in 2018. GPT was designed to generate human-like text by predicting the next word in a given sequence based on the context of the words that came before it. GPT was trained on a massive dataset of web pages, allowing it to learn the structure and patterns of human language, including expressions unique to different languages used by humans.
GPT was followed by GPT-2 in 2019, which improved upon the original GPT model by using a larger and more diverse dataset, as well as a more advanced architecture. GPT-2 was able to generate highly coherent and human-like text, leading to widespread excitement and interest in the field of AI and NLP.
The release of GPT-3 in 2020 was a major milestone in the field of AI and NLP. GPT-3 is the largest language model ever developed, with 175 billion parameters (compared to the 1.5 billion parameters of GPT-2) making it almost 17 times larger than its predecessor (GPT-2). Parameters in the deep neural network mean the weights and biases of the layers of artificial neurons the network is made of. This massive size allows GPT-3 to perform a wide range of NLP tasks, including translation, summarization, question answering, and even writing code.
Key differences between GPT-3 and ChatGPT
While ChatGPT is based on the GPT-3 model, it has been specifically designed and optimized for chatbot applications. ChatGPT uses a variant of the GPT-3 architecture that is tailored for generating responses to user inputs in a conversational context.
One key difference between GPT-3 and ChatGPT is the type of input and output they are designed to handle. GPT-3 is designed to accept a large block of text as input and generate a corresponding output, while ChatGPT is designed to accept a small piece of text (such as a question or statement) and generate a response. This means that ChatGPT is better suited for handling short, conversational exchanges with users, rather than generating large blocks of text like GPT-3.
Another key difference between the two models is the type of tasks they are best suited for. GPT-3 is a general-purpose language model that can perform a wide range of NLP tasks, including translation, summarization, question answering, and even writing code. ChatGPT, on the other hand, is specifically designed for chatbot applications and is best suited for tasks such as answering user questions, providing information, and engaging in natural, human-like conversation.
In terms of technical details, GPT-3 and ChatGPT are both transformer-based language models. This means that they use a type of neural network architecture known as a transformer to process and generate text. However, ChatGPT uses a variant of the GPT-3 architecture that is tailored for generating responses to user inputs in a conversational context.
Internal technical details of how ChatGPT works
In this section, we will delve into somewhat technical details of how ChatGPT works, including its underlying architecture and training process. Like its predecessor, GPT-3, ChatGPT is a transformer-based language model. This means that it uses a type of neural network architecture known as a transformer to process and generate text. The transformer architecture was introduced in a 2017 paper by researchers at Google and has since become a popular choice for NLP tasks due to its ability to handle long-range dependencies in text.
Like other transformer-based models, ChatGPT is composed of multiple layers of self-attention and feed-forward networks. The self-attention layers allow the model to consider the context of each word in a given input, while the feed-forward layers are used to transform the output of the self-attention layers into a prediction for the next word in the sequence.
To train ChatGPT, OpenAI used a variant of the Masked Language Model (MLM) training method that was used to train GPT-3. In the MLM training method, a portion of the input words are randomly masked and the model must predict the masked words based on the context provided by the unmasked words. This allows the model to learn the structure and patterns of language by predicting the missing words in a given sequence.
# examples of masked training dataset; mask is represented by blank (____)
# format: <training input> | <possible output>
US is the _____ democracy | oldest
Earth has one natural sattelite, named as ____ | Moon
70% of Earth's _____ is covered with water | surface / land
In addition to the MLM training method, ChatGPT was also trained on a large dataset of conversational exchanges. This dataset includes a wide variety of conversations, including customer service inquiries, general Q&A, and casual conversations. This training allows ChatGPT to learn the structure and patterns of natural human conversation, allowing it to generate responses that are more coherent and human-like.
One of the key advantages of ChatGPT is its ability to generate responses that are contextually relevant to the user's input. This is achieved through the use of the transformer architecture, which allows the model to consider the context of each word in the input when generating a response. For example, if a user asks a chatbot What is the weather like today? , ChatGPT would be able to consider the context of the words weather and today to generate a relevant response, such as It is sunny and warm today.
In summary, ChatGPT is a powerful NLP model that is specifically designed for chatbot applications. Its transformer-based architecture and extensive training on conversational data allow it to generate responses that are contextually relevant and human-like.