Open Artificial Knowledge

Richard H. Schreiber; Vadim Borisov

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Open Artificial Knowledge

Vadim Borisov, Richard H. Schreiber

Computation and Language · Submitted 19 Jul 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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.

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What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.41 · human participants 0.05 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.94 · compares with prior results 0.08 · mentions code or data availability 0.94.

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arXiv listing abstract v1. Results and the main claim are underlined; show every role.

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	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.	background 1.00	0.05	0.03	asserted 1.00
s1	However, acquiring high-quality, diverse, and ethically sourced training data remains a significant challenge.	gap 0.70	0.07	0.04	asserted 1.00
s2	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.	contribution 1.00	0.91	0.20	asserted 1.00
s3	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.	method 0.98	0.88	0.08	asserted 1.00
s4	Our methodology ensures broad knowledge coverage while maintaining coherence and factual accuracy.	method 0.64	0.70	0.12	asserted 1.00
s5	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.	contribution 0.63	0.55	0.49	asserted 0.97

What kind of paper the classifier reports this is: dataset or benchmark 1.00 · evidence from simulation or computation 0.43.

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arXiv listing abstract v1

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full text
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

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.

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1 arXiv paper cite this in 1 sentence

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it

Cites it as background 1 paper

The use of large language models for the generation of synthetic data (sometimes artificial data [ref] ) has shown promise in a range of NLP tasks, including instruction tuning [ref] , data enhancement [ref] , and low-resource language processing.

Multilingual Multi-Label Emotion Classification at Scale with Synthetic Data · Related Work · 0.84

What it cites · 28 sentences without a confident label

Uses the method or tool of 5 papers

* Subtopic Expansion These high-level topics are expanded with subtopics through advanced language models such as OpenAI's GPT-4o [ref] , improving further the C1 and Quality (C2) by adding depth and breadth that mimic real-world data variability.

GPT-4 Technical Report · OAK Dataset · 1.00
Furthermore, we fine-tune the ELECTRA model [ref] on publicly available toxicity datasets [ref] to provide a filtering score.

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators · OAK Dataset · 0.97
For the Evaluation Metrics (C8) challenge, we will engage the community to fine-tune an LLM on the OAK dataset and evaluate it using common benchmarks such as WinoGrande [ref] , ARC Easy [ref] , and so forth, the list of the benchmarks will be extended.

Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge · OAK Dataset · 0.89

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