Saturday 08 March 2025
The quest for high-quality training data has been a long-standing challenge in the development of large language models (LLMs). These powerful machines can only become as sophisticated as the data they’re trained on, making it crucial to find reliable sources that meet their demanding standards. Two recent papers have made significant strides in addressing this issue by creating comprehensive datasets that are not only freely available but also carefully curated.
The first paper, published by EleutherAI, presents a dataset called Common Pile, which is designed to be a default training set for LLMs. To achieve this, the authors scoured the internet for publicly available content that is licensed under permissive terms, such as Creative Commons licenses or is in the public domain. This includes everything from books and academic papers to news articles and YouTube transcripts.
The sheer scale of Common Pile is impressive, with over 259 million pages of text comprising more than 221 billion words. To put this into perspective, a typical language model might require tens of thousands of hours of training data to achieve state-of-the-art results – and yet, Common Pile provides enough material for multiple models to train simultaneously.
But quality is just as important as quantity when it comes to training data. EleutherAI has implemented a range of filters and heuristics to ensure that the content in Common Pile is accurate, relevant, and free from bias. This includes removing duplicates, filtering out low-quality OCR text, and verifying licenses.
The second paper, published by Pleias, takes a different approach by focusing on conversational data. Their dataset, YouTube-Commons, consists of transcripts from videos that are licensed under Creative Commons’ CC-BY license. While this may seem like a smaller scope than Common Pile, it’s actually a crucial piece of the puzzle.
LLMs trained solely on formal texts can struggle to understand everyday language and nuances of human communication. By incorporating conversational data into their training, these models can learn to recognize patterns and structures that are unique to spoken language. YouTube-Commons provides a treasure trove of such data, with millions of hours of video content available for analysis.
Both Common Pile and YouTube-Commons have the potential to revolutionize the field of LLM development. By providing high-quality, freely available training data, they can enable researchers and developers to create more accurate, more nuanced, and more human-like language models.
Cite this article: “Breakthroughs in Large Language Model Training Data”, The Science Archive, 2025.
Large Language Models, Training Data, Common Pile, Eleutherai, Youtube-Commons, Conversational Data, Public Domain, Creative Commons Licenses, Ocr Text, Bias-Free
