Data Sources for LLMs

Although this event has already taken place, we’re excited to offer future sessions on the same topic! If you’re interested in attending our upcoming seminar with updated content, sign up below, and we’ll notify you when the next event is scheduled.

This event focuses on the diverse and vast data sources used to train Large Language Models, and why quality data is essential for their performance.

Subtopics:

1. What Makes a Good Data Source for LLMs?
   Learn the criteria for selecting data sources, focusing on diversity, relevance, and quality to ensure comprehensive training for LLMs.
2. Types of Data Used for LLM Training
   Explore the different types of data used to train LLMs, including text from books, websites, academic papers, and social media.
3. Web-Crawled Data – The Backbone of LLMs
   Understand how large-scale web-crawled data serves as the foundation for LLM training, offering vast linguistic and contextual diversity.
4. Specialized Datasets for LLMs
   Learn about domain-specific datasets, such as medical or legal texts, that enhance the specialized abilities of LLMs in certain fields.
5. Ethical Considerations in Data Collection
   Discuss the ethical implications of using web data, including privacy concerns, data bias, and the importance of responsible data handling.
6. Preprocessing and Cleaning Data for LLMs
   Explore the preprocessing steps required to clean and filter raw data for LLM training, ensuring high-quality input for optimal performance.
7. Data Augmentation Techniques
   Learn about data augmentation methods that enrich existing datasets, helping LLMs generalize better to new contexts and tasks.

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