Top Sources for Finding LLM Training Data

In this article, you will discover:

• What LLM training data is
• Why LLMs need tons of data to be trained
• The steps required to train an LLM
• The best sources for gathering data for LLM training

Let’s dive in!

What Constitutes Good LLM Training Data?

Good LLM training data must be high-quality, diverse, and pertinent to the intended application. Ideally, it should embrace a broad range of topics, styles, and contexts—which helps the large language model learn varied language patterns.

The right sources depend on the specific goal of the LLM. Still, commonly used sources include web pages, books, video transcripts, online publications, research articles, and code archives. Together, these provide a broad representation of human language and knowledge.

What truly makes a difference is that the data must be clean and free of noise, such as irrelevant text or formatting errors. It should also be balanced to reduce biases, allowing the model to learn accurately and generate better, more reliable outputs.

Why LLMs Need a Lot of Data

To achieve a high level of complexity, nuance, and accuracy, LLMs require huge amounts of data. The main reason is that their ability to understand human language and produce relevant responses hinges on exposure to multiple language patterns, topics, and contexts.

Feeding an LLM with a large volume of data enables it to grasp subtle relationships, develop a strong understanding of context, and accurately predict likely word sequences. This ultimately improves the model overall effectiveness.

That data is typically extracted from public sources, as these reflect the breadth of human knowledge and communication styles—without raising privacy or regulatory issues. However, for specific applications, private or custom datasets may be used to fine-tune the model—provided they comply with privacy standards.

In short, here are the main reasons why more data leads to better-performing LLMs:

• Enhanced knowledge base: Just as humans become more knowledgeable with access to a lot of information, the more topics covered in training data, the more likely the model will be to generate relevant responses across several domains.
• Diverse language patterns: Access to a number of writing styles and perspectives gives the model the ability to learn nuanced linguistic patterns. That improves its contextual understanding, even across multiple languages.
• Reduced bias: Larger data sets tend to be less biased than smaller ones, increasing the likelihood that the LLM will produce more objective results.
• Enhanced responses: With exposure to a lot of data, the LLM can become more effective in recognizing language rules and relationships between words, reducing the frequency of errors.
• Factual responses: Data from fresh content helps the model stay aligned with the latest information, supporting more relevant and up-to-date responses.

How to Train an LLM on Custom Data

Suppose you gathered a lot of data from different sources—which you will learn about soon. What steps should you follow to train your LLM? Time to find out!

Step#1: Data Collection and Preprocessing

• Data sourcing: The first step in training any LLM is collecting data—a lot of LLM training data. This data is usually obtained from a set of public (and sometimes private) sources. For more details, check out our guide on data sourcing.
• Preprocessing: After collecting the raw data, you must clean it to prepare it for training. Note that existing AI tools like ChatGPT can be used during this process, which includes:
  • Text cleaning: Removing irrelevant content, duplicate entries, and noise.
  • Normalization: Converting the text to lowercase, removing stop words, and addressing other formatting inconsistencies.
  • Tokenization: Breaking down the text into smaller units such as words, subwords, or characters, which will be used by the model during training.

Step#2: Choosing or Creating the Model

• Pre-trained models: For most LLM projects, using a pre-trained model like GPT, BERT, or T5 is considered the recommended approach. These solutions have already learned most general language patterns, and you only need to fine-tune them for specific objectives with custom data. For a guided approach, take a look at how to create a RAG chatbot with GPT-4 using SERP data.
• Custom model: If pre-trained models do not suit your needs or if you have unique requirements, you can create a new model from scratch. Tools like PyTorch, LangChain, and TensorFlow can be used to build and train LLMs. Keep in mind that this route requires considerable computing resources and a lot of money.

Step#3: Model Training

• Pre-training: If you opt for creating your own model, pre-training is key. During this phase, the model learns general language patterns and the structure of the language. The LLM is normally trained by predicting missing words or tokens in a sequence, which helps it learn context and grammar.
• Fine-tuning: After pre-training, fine-tuning adjusts the model for specific tasks, such as answering questions, summarizing text, or translating languages. Fine-tuning is often done using smaller, domain-specific datasets. It may also involve supervised learning, reinforcement learning, and human-in-the-loop methods.

Step#4: Testing and Evaluation

• Testing: Once the model has been trained, the next step is to evaluate its performance using metrics like accuracy, perplexity, BLEU score, or F1 score—depending on the task at hand. The idea here is to ensure that the model’s outputs are both accurate and relevant to its intended use case.
• Hyperparameter tuning: During testing, you might need to adjust some hyperparameters, such as learning rates, batch sizes, and gradient clipping. This process usually takes an iterative approach with many trials and adjustments, but it is essential to optimize the model’s performance.

Step#5: Deployment and Monitoring

• Deploying the model: Once the model is trained, tested, and optimized, you must deploy it for real-world use. That could involve integrating the model into applications, systems, or services that can benefit from its capabilities. Example of such applications are chatbots, virtual assistants, and content-generation tools.
• Continuous monitoring: After deployment, ongoing monitoring is vital to make sure that the model maintains its performance over time. Periodic retraining with fresh data can help the model stay up to date and improve its outputs as more information becomes available.

Best Sources for Retrieving LLM Training Data

You are now aware that data is what makes all the difference when it comes to LLM training. So, you are ready to explore the best sources for gathering LLM training data, categorized by source type.

Web Content

The Web is unsurprisingly the richest, largest, and most used source of data for LLM training. Extracting data from web pages is a process known as web scraping, which helps you gather large amounts of data.

For instance, social networks like X, Facebook, and Reddit contain conversational data. Wikipedia hosts over 60 million pages on a wide range of topics. E-commerce sites like Amazon and eBay feature valuable data through product descriptions and reviews. This type of information is invaluable for training LLMs to understand sentiment and everyday language. Here is why popular LLMs like GPT-4 and BERT rely heavily on web data.

When it comes to scraping data from the Internet, you have two options:

1. Build your own scraper
2. Purchase a comprehensive ready-to-use dataset

Whether you choose one or the other approach, Bright Data has you covered. With dedicated a Web Scraper API designed to retrieve fresh data from over 100 sites and an extensive dataset marketplace, it gives you access to everything you need for effective LLM training data collection.

Scientific Discussions

Sites like Stack Exchange and ResearchGate allow researchers, practitioners, and enthusiasts to ask questions, share knowledge, and discuss various topics. These span across multiple fields, including mathematics, physics, computer science, and biology.

The scientific discussions on these platforms are highly valuable for training LLMs to recognize complex technical questions and guarantee in-depth answers.

Research Studies

Research papers can give LLMs specialized knowledge in medicine, technology, economics, engineering, finance, and more. Sources like Google Scholar, ResearchGate, PubMed Central, and PLOS ONE offer access to peer-reviewed papers. These introduce new ideas, concepts, and methodologies in their respective disciplines.

Those documents contain technical jargon and complex topics, making them ideal for training LLMs on professional and/or scientific domains.

Books

Books are an excellent resource for training LLMs, particularly when it comes to learning formal language. The problem is that most books are protected by copyright, which can limit their use. Fortunately, there are public domain books available that can be freely accessed and used.

For example, Project Gutenberg counts over 70,000 free ebooks across a wide range of genres. These cover many topics, making the LLM knowledgeable on philosophy, science, literature, and more.

Code Content

If your LLM should also be able to handle programming tasks, feeding it with code is a necessary step. Platforms like GitHub, Stack Overflow, Hackerrank, GitLab, and DockerHub host thousands of repositories of code and programming questions.

GitHub alone stores millions of open-source code repositories in a wide array of programming languages, from Python and JavaScript to C++ and Go. By training on this code, LLMs can learn how to generate code, debug errors, and understand the syntax and logic behind programming languages.

News Outlets

Google News, Reuters, BBC, CNN, Yahoo News, Yahoo Finance, and other major media sites have articles, reports, and updates on a wide range of topics. These cover politics, economics, health, entertainment, and more. Follow our article on how to scrape Yahoo Finance.

News articles help LLMs understand the evolving nature of language. They also offer key insights into regional language variations, tone, and structure, as different outlets may cater to different audiences. Plus, this LLM training data is essential for the model to stay abreast of current events and global trends.

Additionally, you can use a Financial Data or News Scraper API or explore our dataset marketplace.

Video Transcripts

Video transcripts are an invaluable resource for training LLMs on conversational language. This data plays a crucial role if the model needs to handle tasks like customer service or support, for example.

Public video platforms such as YouTube, Vimeo, and TED Talks all come with a wealth of transcribed content across a wide variety of topics. These transcripts capture natural conversations, speeches, and lectures—providing rich LLM training data. See our tutorial on how to scrape data from YouTube.

Conclusion

In this article, you explored what makes quality LLM training data, where to retrieve it, and how to use it to train a large language model. Regardless of the approach you take, the first step is to gather a lot of data. In this game, the Web is the most valuable source to tap into.

Bright Data is one of the most reputable providers of data for AI training on the market. It offers comprehensive solutions to easily discover, collect, and manage web data at scale. From pre-training to fine-tuning your models, it provides continuously refreshed, clean, validated, compliant, ethical, and scalable data.

Bright Data’s solutions for LLM training data retrieval include:

• Datasets: Pre-collected, clean, and validated datasets containing over 5 billion records across 100+ popular domains.
• Scraper APIs: Dedicated endpoints designed for efficient scraping of top domains.
• Serverless Scraping: Tools for simplified data collection with optimized performance.
• Datacenter Proxies: High-speed, reliable proxies to support web scraping.

Sign up now and explore Bright Data’s datasets, including a free sample.