How are datasets curated for LLM training?

Datasets for Large Language Model (LLM) training are curated through a process that involves:

1. Data collection: Gathering text data from various sources, such as books, articles, websites, social media platforms and with the help of training data service providers.

2. Data cleaning: Removing unnecessary characters, punctuation, and formatting.

3. Tokenization: Breaking down text into individual tokens, such as words or subwords.

4. Filtering: Removing duplicates, special characters, and irrelevant text.

5. Preprocessing: Normalizing text, converting to lowercase, and removing stop words.

6. Balancing: Ensuring the dataset is balanced in terms of topic, style, and genre.

7. Anonymization: Removing personal information and sensitive data.

8. Quality control: Human evaluation to ensure the dataset is accurate and relevant.

9. Splitting: Dividing the dataset into training, validation, and test sets.

10. Versioning: Keeping track of dataset versions and updates.

The goal is to create a diverse, representative, and high-quality dataset that enables LLMs to learn effective language understanding and generation capabilities.