The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text
This addresses ethical and legal concerns for AI researchers and developers by providing a high-quality, openly licensed dataset for LLM pretraining, though it is incremental as it builds on prior data collection efforts.
The authors tackled the problem of training large language models (LLMs) on unlicensed text by creating the Common Pile v0.1, an 8TB dataset of openly licensed text, and showed that models trained on it achieve competitive performance to those trained on unlicensed text, with 7B parameter models matching Llama 1 and 2 7B.
Large language models (LLMs) are typically trained on enormous quantities of unlicensed text, a practice that has led to scrutiny due to possible intellectual property infringement and ethical concerns. Training LLMs on openly licensed text presents a first step towards addressing these issues, but prior data collection efforts have yielded datasets too small or low-quality to produce performant LLMs. To address this gap, we collect, curate, and release the Common Pile v0.1, an eight terabyte collection of openly licensed text designed for LLM pretraining. The Common Pile comprises content from 30 sources that span diverse domains including research papers, code, books, encyclopedias, educational materials, audio transcripts, and more. Crucially, we validate our efforts by training two 7 billion parameter LLMs on text from the Common Pile: Comma v0.1-1T and Comma v0.1-2T, trained on 1 and 2 trillion tokens respectively. Both models attain competitive performance to LLMs trained on unlicensed text with similar computational budgets, such as Llama 1 and 2 7B. In addition to releasing the Common Pile v0.1 itself, we also release the code used in its creation as well as the training mixture and checkpoints for the Comma v0.1 models.