File List

In the context of large language models (LLMs), a "file list" refers to a structured collection of text files that are used to train the model, essentially providing the LLM with a vast amount of data to learn patterns and relationships from, allowing it to generate text, translate languages, answer questions, and perform other language-related tasks based on the information contained within those files. 

Key points about file lists for LLMs:

• Data diversity:

  A good file list will include a variety of text sources like books, articles, code, news articles, and web pages to ensure the LLM can understand a broad range of topics and writing styles. 

• Data cleaning:

  Before training, the file list often undergoes cleaning to remove irrelevant or low-quality data, ensuring the LLM learns from accurate and reliable information. 

• File formats:

  Depending on the LLM framework, the file list may be formatted in specific ways, such as plain text files, or may require specific metadata like document titles or categories. 

How file lists are used in LLM training:

• Feeding data:

  The LLM is trained by iterating through the files in the list, processing each text segment and learning patterns within the data. 

• Fine-tuning:

  Specific subsets of the file list can be used to fine-tune the LLM for specific tasks, like generating medical text or writing code, by focusing the training on relevant data.