Title: Common Corpus: The Largest Collection of Ethical Data for LLM Pre-Training
Primary area: datasets and benchmarks
Keywords: dataset, pre-training, large language models, open data, open science, multilingual

Abstract: Large Language Models (LLMs) are pre-trained on large data from different sources and domains. These data most often contain trillions of tokens with large portions of copyrighted or proprietary content, which hinders the usage of such models under AI legislation. This raises the need for truly open pre-training data that is compliant with the data security regulations. In this paper, we introduce Common Corpus, the largest open dataset for LLM pre-training. The data assembled in Common Corpus are either uncopyrighted or under permissible licenses and amount to about two trillion tokens. The dataset contains a wide variety of languages, ranging from the high-resource European languages to some low-resource languages rarely represented in pre-training datasets. In addition, it includes a large portion of code data. The diversity of data sources in terms of covered domains and time periods opens up the paths for both research and entrepreneurial needs in diverse areas of knowledge. In this paper, we present the detailed provenance of data assembling and the details of dataset filtering and curation. We train two small language models on Common Corpus and find that the resulting model performs comparably to other models of their size, indicating that our dataset is suitable for multilingual pretraining. Common Corpus represents a key contribution to the ecosystem for open science research on large language models.

TL;DR: We assemble and release the largest truly open multilingual dataset for LLM pre-training consisting of 2 trillion tokens