  The tremendous success of chat-based AI systems like ChatGPT, Claude, and
Gemini stems from Large Language Models (LLMs) trained on vast amount of
datasets. However, acquiring high-quality, diverse, and ethically sourced
training data remains a significant challenge. We introduce the Open Artificial
Knowledge (OAK) dataset, a large-scale resource of over 500 million tokens (at
the moment of writing) designed to address this issue. OAK leverages an
ensemble of state-of-the-art LLMs, including GPT4o, LLaMa3-70B, LLaMa3-8B,
Mixtral-8x7B, Gemma-7B, and Gemma-2-9B , to generate high-quality text across
diverse domains, guided by Wikipedia's main categories. Our methodology ensures
broad knowledge coverage while maintaining coherence and factual accuracy. The
OAK dataset aims to foster the development of more capable and aligned language
models while addressing critical issues of data scarcity and privacy in LLM
training, and it is freely available on www.oakdataset.org.
