What is GPT-3? Key Concepts & Applications

In this article, we'll introduce GPT-3: its key concepts and applications. GPT-3 is a large natural language understanding model that can do a variety of tasks such as machine translation, question answering, and text generation.

a month ago   •   6 min read

By MLQ

In this article, we'll introduce GPT-3: its key concepts and applications. Specifically, we'll discuss:

  • What is GPT-3?
  • Applications of GPT-3
  • How does GPT-3 work
  • What are the benefits of using GPT-3?
  • What are the limitations of GPT-3?
  • GPT-3 Models You Can Use
  • How to use GPT-3
  • How to use the GPT-3 prompt gallery
  • How to Fine-Tune GPT-3 for Your Application

What is GPT-3?

GPT-3 is a large natural language understanding model that can do a variety of tasks such as machine translation, question answering, and text generation.

GPT-3 has been making waves in the AI community since its release in late 2019. This natural language understanding model, or large language model, has captured the attention of the media for its ability to generate realistic text.

What sets GPT-3 apart from other language models is its ability to generate text that is not just grammatically correct, but also sounds like it was written by a human.

The model was trained on a dataset of hundreds of billion words, and it is said to be the largest neural network of its kind.

You can find the original GPT-3 paper here.

Applications of GPT-3

Text Generation

As mentioned, GPT-3 can generate text that is indistinguishable from human-written content. This has applications in everything from creating realistic dialogue for video games to generating fake news articles.

Machine Translation

GPT-3 can also be used for machine translation, and is already being integrated into popular translation applications like DeepL. With GPT-3, DeepL is able to translate texts from English to other languages with greater accuracy and fluency than ever before.

Question Answering

GPT-3 can be used to answer questions, making it a valuable tool for research and customer service applications. For example, GPT-3 was used by OpenAI to develop 'Composer', a question-answering bot that can generate detailed, long-form answers to questions about complex topics like history and literature.

Language Analysis

In addition to its ability to generate text, GPT-3 can also be used for language analysis. For example, GPT-3 was used by the Allen Institute for Artificial Intelligence to develop 'Grover' that can automatically identify the tone of a piece of text.

How does GPT-3 work?

GPT-3 uses a deep-learning architecture known as a transformer. Transformers were first introduced in the paper "Attention is all you need" written by members of the Google Brain team, and released in 2017.

The transformer architecture is based on the idea of self-attention, which allows the model to focus on specific parts of the input text while ignoring others. This makes transformers much more efficient than previous architectures, and allows GPT-3 to learn from a far larger amount of data.

In order to train GPT-3, OpenAI used a dataset of over 4.5 billion words. This dataset was generated by scraping texts from the internet and includes a wide variety of content from different sources.

Previous architectures relied on a recurrent neural network (RNN), which reads the input text one word at a time. This is often inefficient, as the RNN has to read the entire input text before it can start understanding it.

The transformer architecture, on the other hand, uses a self-attention mechanism, which allows the model to focus on specific parts of the input text while ignoring others. This makes transformers much more efficient than previous architectures and allows GPT-3 to learn from a far larger amount of data.

In the transformer architecture, each item in the input sequence is represented by a vector, which is then transformed by a series of matrix operations. The vectors are passed through several "attention heads", which learn to focus on different parts of the input sequence.

The attention heads are combined to produce a final vector, which is then transformed into the output sequence. This process is repeated for every item in the input sequence.

The training process for GPT-3 took several weeks, during which time the model was able to learn the grammar and structure of English.

What are the benefits of using GPT-3?

GPT-3 has several advantages over previous NLU models:

1. It is much larger, which gives it more "learned" knowledge.

2. It is available through an API, which makes it easier to use.

3. It can be "fine-tuned" for specific applications.

What are the limitations of GPT-3?

GPT-3 has several disadvantages:

1. It is much larger, which makes it more expensive to train and use.

2. It is not yet available for commercial use.

3. It is not perfect, and can sometimes generate fake text that is indistinguishable from real text.

GPT-3 Models You Can Use

GPT-3 is available in four different sizes, each with a different number of parameters:

  • Ada: 12 billion parameters
  • Babbage: 24 billion parameters
  • Curie: 48 billion parameters
  • Davinci: 176 billion parameters
  • Eve: 768 billion parameters

The largest GPT-3 model, Eve, has nearly double the number of parameters as the second largest NLU model, Google's Turing-NLG.

How to use GPT-3

GPT-3 can be accessed through an API, which allows developers to build applications that use the GPT-3 models.

In order to use the API, developers need to first create a key that gives them access to the API. Keys can be created through the OpenAI website.

Once a key has been created, developers can then use the API to access the GPT-3 models. The API allows developers to specify the size of the model they want to use, as well as the task they want the model to perform.

The API also allows developers to "fine-tune" each GPT-3 model for their specific applications. For example, a machine translation application might fine-tune the Curie model for French-to-English translation.

Once a key has been created, developers can use the "curl" command to send requests to the API. For example, the following command will generate text using the "small" GPT-3 model:

curl -X POST -H "Content-Type: application/json" -d '{"text":"The quick brown fox jumps over the lazy dog."}' https://api.openai.com/v1/engines/ada/completions?max_tokens=32&temperature=0.7&top_p=1.0

The response will be a JSON object containing the generated text.

The GPT-3 prompt gallery is a website that allows users to browse, search, and share "presets" for the GPT-3 model.

A preset is a set of input parameters that allow the GPT-3 model to generate text for a specific task or application. For example, there are presets for machine translation, question answering, and text generation.

The gallery also allows users to create their own custom presets, and share them with other users.

How to fine-tune GPT-3 for your application

Developers can also fine-tune GPT-3 using their own data in order to design a custom version specifically for their application. This allows for a more efficient use of the GPT-3 model, and can improve results.

To fine-tune GPT-3, developers need to first create a dataset of training examples. This dataset can be created manually, or by using a web crawler.

Once the dataset has been created, developers can use the "curl" command to send requests to the API. These requests will return a JSON object, which can be used to train the model.

If you're interested in using GPT-3 for your own application, the first step is to create a dataset of training examples. This dataset can be created manually, or by using a web crawler.

Learn more

Customizing GPT-3 for Your Application
Fine-tune with a single command.

Summary: What is GPT-3?

GPT-3 is the largest NLU model in the world, and is capable of performing several language tasks such as machine translation, question answering, and text generation.

Over 1.6 billion words were used in the dataset that OpenAI used to train GPT-3. This dataset, which contains a wide range of content from many sources, was created by scraping texts from the internet.

The GPT-3 models are available through an API, which allows developers to build applications that use the GPT-3 models. Keys can be created through the OpenAI website.

The API also allows developers to "fine-tune" the GPT-3 models for their specific applications. For example, a machine translation application might fine-tune the Curie model for French-to-English translation.

And yes, if you were wondering, this article was written largely by GPT-3 :)

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