Summary of Open Artificial Knowledge, by Vadim Borisov et al.
Open Artificial Knowledge
by Vadim Borisov, Richard H. Schreiber
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces the Open Artificial Knowledge (OAK) dataset, a large-scale resource of over 500 million tokens designed to address the challenge of acquiring high-quality, diverse, and ethically sourced training data for Large Language Models (LLMs). The OAK dataset 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. The 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a big project called Open Artificial Knowledge (OAK) that helps make chat-based AI systems better. This project creates a huge library of text from various areas, like Wikipedia, to help train language models. The goal is to make these models more useful and honest while keeping users’ data private. |
