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How Much Data Did ChatGPT Use to Do Its Training?

ChatGPT, a powerful language model-based chatbot developed by OpenAI, has revolutionized the field of conversational AI. With its advanced capabilities, ChatGPT can refine and steer conversations towards desired lengths, formats, styles, levels of detail, and even languages used. One of the key factors contributing to the impressive abilities of ChatGPT is the vast amount of data it was trained on. In this blog, we will delve into the depths of ChatGPT's training data, exploring its sources and the massive scale on which it was collected.

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Published onAugust 24, 2023
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How Much Data Did ChatGPT Use to Do Its Training?

ChatGPT, a powerful language model-based chatbot developed by OpenAI, has revolutionized the field of conversational AI. With its advanced capabilities, ChatGPT can refine and steer conversations towards desired lengths, formats, styles, levels of detail, and even languages used. One of the key factors contributing to the impressive abilities of ChatGPT is the vast amount of data it was trained on. In this blog, we will delve into the depths of ChatGPT's training data, exploring its sources and the massive scale on which it was collected.

Training on an Enormous Scale

To develop the sophisticated language understanding and generation capabilities of ChatGPT, OpenAI trained the model on an enormous amount of text data. According to the OpenAI Cookbook, ChatGPT was trained on over 45 terabytes of text data. This massive corpus includes a wide range of sources, such as books, articles, web pages, and various other text formats.

The dataset used for training ChatGPT was a combination of structured and unstructured data, allowing the model to learn from diverse text types and formats. This diverse training data plays a crucial role in enabling ChatGPT to generate contextually relevant and coherent responses.

A Glimpse into the Training Process

During the training process, a subset of the data is typically selected to train the language model. For GPT-3, the base model of ChatGPT, the training data covered the period from 2016 to 2019. This subset of data underwent compression, resulting in 45 terabytes of compressed plain text. After filtering, the dataset was reduced to 570 gigabytes.

The sources of the training data encompass a wide range of content. Sources such as books, articles, research papers, and web pages were used to create a comprehensive and diverse training corpus. The inclusion of such varied sources helps ChatGPT to grasp the nuances of different writing styles and genres.

The Role of Stack Overflow Data

One question that often arises is whether ChatGPT was trained on Stack Overflow data. Stack Overflow is a popular platform for programmers to ask and answer technical questions. However, it does not appear that Stack Overflow data was directly used to train ChatGPT.

In a discussion on AI Stack Exchange, the question of whether highly-rated and upvoted questions/answers from Stack Overflow were part of ChatGPT's training data was raised. However, there is no explicit mention of Stack Overflow data being used in the training of ChatGPT. Instead, the sources mentioned earlier, such as books, articles, and web pages, formed the primary basis for the extensive training of ChatGPT.

The Cost and Time of Training

Training a language model on such a vast amount of data is a resource-intensive process. In the case of ChatGPT, the training cost was reported to be \$43,000. This cost reflects the significant computational resources required to process and train the model on such a large-scale dataset.

Additionally, the training process itself is time-consuming. While the specific duration for training ChatGPT is not mentioned, it is safe to assume that training a language model of this magnitude requires a considerable amount of time and computational power.

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