Have you been seeing a lot of chatter about AI language models on social media lately? You’re not alone! ChatGPT has helped bring these powerful tools into the mainstream. But ChatGPT is just one example of the fantastic work being done at OpenAI. You may have heard of another one of their natural language processing models: GPT-3. Released over two years ago, GPT-3 has made waves in the AI community. What are the main similarities and differences between ChatGPT vs GPT-3? We will break that down in this article!
ChatGPT vs GPT-3
- Model Type
- Unsupervised Learning
- Training Data Size
- Type of Training Data
Want to learn more about each of these in the list? Let’s dive into more detail!
What is GPT-3?
Before jumping into the differences between ChatGPT and GPT-3 let’s break down each individually, starting with the GPT-3.
First released in June 2020, GPT-3 (Generative Pre-Trained Transformer 3) revolutionizes how the world looked at AI language models. This was not OpenAI’s first language model, but it was by far the most advanced they have ever created.
OpenAI trained the model on an extensive text dataset to create the model. The dataset had over 175 billion unique parameters making it one of the most significant models ever trained. Not only was the training data itself massive, but it also came from various sources, including website articles, books, and other website material.
GPT-3 can be fine-tuned for multiple language tasks, including translation, summarization, and question answering. It works by processing a large dataset of text and using machine learning techniques to learn the patterns and structures of the language.
One of the main advantages of GPT-3 is its ability to generate coherent and fluent text. This makes it useful for various applications, including chatbots, content generation, and language translation.
GPT-3 was one of the main drivers for pushing AI to the mainstream through OpenAI’s Playground. In the playground, people can play around with building and fine-tuning prompts for their specific use case.
GPT-3 also increased the increase of AI companies built through their public GPT-3 API. This API allows entrepreneurs to use this model in their applications quickly. Right now, several great startups are using this technology. Check out our list of the top 10 GPT-3 Tools and Applications.
What is ChatGPT?
Now let’s jump into what is OpenAI’s ChatGPT. Launched over two years later, ChatGPT was released to the public on November 30th, 2022. ChatGPT is a variant of GPT-3 and is also an AI language model using similar technologies.
ChatGPT was trained on a massive model of 1.5 billion parameters. The dataset includes social media, books, and news articles. This dataset was focused on conversational logs so that the model could generate appropriate and coherent responses in the context of a conversation.
Like GPT-3, ChatGPT works by processing a large dataset of text and using machine learning techniques to learn the patterns and structures of the language. However, ChatGPT has been specifically designed and trained to generate responses that are suitable for use in a chatbot setting.
One of the great features of ChatGPT is its easy-to-use interface. Past chat logs are logged and recorded on the left side of the screen. They are making it easy for someone to just right back into a conversation later on.
ChatGPT’s Response to ChatGPT vs GPT-3
Since we are talking about language models here, I thought that before we get into my response, we should first ask ChatGPT. What is the difference between ChatGPT and GPT-3?
This is what ChatGPT had to say:
GPT-3 (Generative Pre-trained Transformer 3) is a state-of-the-art language generation model developed by OpenAI. It has a massive scale, with 175 billion parameters, and can generate high-quality text that is difficult to distinguish from text written by humans. It can be fine-tuned for various language tasks, including translation, summarization, and question answering.
ChatGPT is a variant of GPT-3 that is specifically designed for generating responses in a chatbot setting. It has been trained on a large dataset of conversation logs and is able to generate responses that are appropriate and coherent in the context of a conversation.
Comparison: ChatGPT vs GPT-3
Similarities: ChatGPT vs GPT-3
1. Language Models
At their core, ChatGPT and GPT-3 are both language models that are designed to generate human-like text. They are highly skilled at processing and understanding language, and can be fine-tuned for specific tasks such as language translation, summarization, and question-answering. This makes them valuable tools for a wide range of applications, from chatbots and virtual assistants to language translation software and more.
2. Unsupervised Learning
Both models use a transformer architecture, which allows them to process language inputs and generate output in a more efficient and effective way. They are also both trained using unsupervised learning, meaning they are not given explicit labels for the input data they are trained on. Instead, they learn to understand language patterns and relationships by analyzing vast amounts of text data.
Differences: ChatGPT vs GPT-3
1. Training Size
When it comes to training artificial intelligence models, data is key. And both GPT-3 and ChatGPT were fed massive amounts of it in order to become the powerful tools they are today. However, the scale of their training data differed significantly.
GPT-3 is a behemoth of a model, with 175 billion parameters under its belt. This massive size allows it to perform a wide range of tasks, from language translation to question answering to generating original text. In contrast, ChatGPT is a smaller model with only 1.5 billion parameters.
2. Type of Training
GPT-3 is truly a marvel of modern artificial intelligence, and a big part of its impressive capabilities can be attributed to the vast amount of data it was trained on. With a dataset comprised of over 8 million web pages, GPT-3 was designed to be a universal language tool, capable of handling a wide range of tasks and inputs.
On the other hand, ChatGPT was specifically trained to excel at conversation. Its training dataset was focused on conversation logs, giving it the ability to hold natural, human-like discussions. While ChatGPT may not have the same versatility as GPT-3, it is a highly effective tool for creating chatbots and virtual assistants that can engage with users in a lifelike manner.
ChatGPT is the go-to choice for chat-based dialogue and conversation, while GPT-3 is a powerful general-purpose language model with a wide range of capabilities.
Because it is more extensive and was trained on a larger and more diverse dataset, GPT-3 generally performs better than ChatGPT on a wide range of language-based tasks. However, ChatGPT may still be a good choice for certain chat-based applications because of its specific design for this task.
To get a better understanding of the differences between ChatGPT and GPT-3, we decided to put them to the test. We headed over to the OpenAI playground and asked each model a series of requests to see how they would respond.
Write a poem in the style of Robert Frost for the college introductory physics class.
Write a lease agreement template.
Please create a product roadmap for Instagram’s story in order to increase the number of posts. Please be as detailed as possible, and whenever possible, use comparisons to other tools such as TikTok.
ChatGPT is a variant of the GPT-3 language model that has been specifically designed and fine-tuned for the task of chatting and conversation. It is able to generate more human-like responses and engage in more natural conversation compared to the original GPT-3 model. However, ChatGPT is not as general-purpose as GPT-3 and may not perform as well on tasks outside of chat and conversation.