How Much Dataset Is Used To Train ChatGPT Explained In Detail

March 26, 2023March 26, 2023 CS Electrical And Electronics

Hello guys, welcome back to our blog. In this article, we will discuss how much dataset is used to train chatGPT, how data was collected, and what technology was used to train ChatGPT.

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Dataset Is Used To Train ChatGPT

A sizable language model called ChatGPT was developed using a sizable text data set for training. The Common Crawl dataset, which is a collection of web pages and text documents from various sources, was used to train the model. According to reports, the dataset utilized to train the most advanced version of GPT-3 (175 billion parameters) was 570GB in size.

There are billions of web pages, documents, and other text sources in the enormous, frequently updated dataset known as The Common Crawl. It contains a wide variety of text data, such as social media posts, books, research papers, and news items.

The dataset is the perfect source of data for training a multilingual language model like ChatGPT because it also contains text in numerous other languages.

In order for ChatGPT to learn the fundamental patterns and structure of language, it was necessary to give the model enormous volumes of text data throughout the training process. The model learns to predict the following word in a series of texts without any particular instruction or supervision from human specialists thanks to a process known as unsupervised learning. Instead, a method known as stochastic gradient descent was used to train the model to maximize a certain objective function (in this case, maximizing the likelihood of the following word given in the preceding text).

It’s significant to notice that ChatGPT’s success was significantly influenced by the number of training samples. The model was able to learn a very wide range of linguistic patterns and structures by training on such a sizable dataset, which enabled it to produce amazingly coherent and varied responses to a variety of prompts and inquiries.

Let’s break down the topics:

01. Common Crawl:

The Common Crawl is the main training dataset for ChatGPT. This database is updated frequently and is maintained by a nonprofit group called the Common Crawl Foundation. It includes text and web pages from numerous sources, including blogs, social media, news websites, and academic publications. The Common Crawl is thought to include more than 60 trillion words in more than 200 languages as of 2021.

02. Other Datasets:

The developers of ChatGPT employed additional datasets in addition to the Common Crawl to optimize the model for particular applications. For instance, they used Wikipedia, which has millions of articles in other languages, and the BookCorpus dataset, which has over 11,000 books in English.

03. Preprocessing:

The initial raw text data was preprocessed in a number of ways before being fed into the model. Tokenizing the text into individual words or subwords, eliminating HTML tags, and transforming the text into a numerical representation that the model could handle were all part of this procedure.

04. Training Method:

Unsupervised learning, a method, was used to train the model. This indicates that the model was taught to anticipate the following word in a text sequence based on the words that came before it rather than being given a specific assignment to do. To maximize the likelihood of the following word given the previous text, the model’s parameters were optimized throughout the training process using a method known as stochastic gradient descent.

Ultimately, ChatGPT’s success was greatly influenced by the richness and diversity of the dataset that was used to train it. The model learned a highly rich and sophisticated grasp of the language by being trained on such a vast amount of text data from a variety of sources and languages, which enables it to produce astonishingly coherent and varied responses to a variety of prompts and inquiries.

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