Large language models (LLMs) learn from huge volumes of data. The core of an LLM is the size of the dataset it’s trained on. But the definition of “large” has been increasing exponentially, and now, these models are typically trained on datasets large enough to include nearly everything that has been written on the internet over a significant span of time.

LLM models are trained on a diverse range of text data, including but not limited to:

1. Webpages: The LLM models use a large corpus of webpages, which includes articles, blog posts, product descriptions, and more. These webpages come from a diverse range of websites, including news, e-commerce, and educational sites, providing the model with a broad range of information, covering topics such as current events, product specifications, and historical facts.

2. Books: The LLM models use a diverse range of books, covering various genres, including fiction, non-fiction, and technical books. The books provide the model with a wealth of information and cover topics such as science, history, literature, and more.

3. News articles: The LLM models use news articles from a variety of sources, including international, national, and local news outlets. These articles provide the model with up-to-date information on current events, politics, sports, and more.

4. Conversational text: The LLM models use conversational text from various sources, including customer support chats, forums, and social media conversations. This training data allows the model to understand and respond to conversational cues, such as questions, requests, and follow-up questions.

5. Other forms of text: The LLM models also use a variety of other forms of text, such as product reviews, scientific papers, legal documents, and more. This training data allows the model to understand and provide information on a wide range of topics and domains, including technical and specialized information.

These training data sources provide the LLM model with a broad base of knowledge, allowing it to respond accurately and comprehensively to a wide range of questions and tasks.

Important resources to get open-source data from your language model

There are many open-source datasets that can be used to train language models, including:

Common Crawl: A large corpus of web pages that can be used to train language models. It contains billions of web pages and is updated monthly.

Wikipedia: The online encyclopedia contains a wealth of information on a wide range of topics, making it a useful source of training data for language models.

Project Gutenberg: A large collection of free e-books that can be used to train language models. The books cover a wide range of genres and topics.

OpenWebText: A collection of over 40GB of text from the web, pre-processed to remove low-quality text, making it a useful source of high-quality training data for language models.

Reddit: A popular social news site that contains a wealth of information on a wide range of topics, making it a useful source of training data for language models.

NewsCrawl: A collection of news articles from a variety of sources, including international, national, and local news outlets, making it a useful source of training data for language models.

Cornell Movie Dialogs Corpus: A dataset of movie scripts and conversations, making it a useful source of conversational training data for language models.

These are just a few examples of open-source datasets that can be used to train language models. By using these datasets, or a combination of them, you can create a well-rounded language model with a broad range of knowledge and understanding.

You can also create custom datasets to train your language model. That can give many advantages over only using open-source data.

There are several advantages to using custom training data when building language models:

A) Domain specificity: Custom training data allows you to focus the language model’s training on specific domains, such as a particular industry or subject matter, leading to more accurate and relevant results when the model is used in these domains.

B) Improved performance: Custom training data can improve the performance of the language model by allowing it to learn from data that is relevant to your specific use case, rather than relying on generic, widely available training data.

C) Increased accuracy: By training a language model on custom data, you can fine-tune it to the unique characteristics of your data, leading to increased accuracy when making predictions and generating outputs.

D) Data privacy: Using custom training data allows you to keep sensitive information private, as the data is not shared with third parties or used for other purposes.

E) Better understanding of the data: By training a language model on custom data, you can gain a deeper understanding of the data and identify patterns or trends that may not be evident with generic training data.

Overall, using custom training data can help you create a language model that is better suited to your specific use case, leading to improved performance, accuracy, and privacy.

https://medium.com/@praveenmali7610/training-data-used-to-train-llm-models-17cfdf190cc6