Version 1
: Received: 11 April 2024 / Approved: 12 April 2024 / Online: 12 April 2024 (12:46:27 CEST)
How to cite:
PS, D.; PS, A.; VG, J. Intellecta Cognitiva: A Comprehensive Dataset for Advancing Academic Knowledge and Machine Reasoning. Preprints2024, 2024040849. https://doi.org/10.20944/preprints202404.0849.v1
PS, D.; PS, A.; VG, J. Intellecta Cognitiva: A Comprehensive Dataset for Advancing Academic Knowledge and Machine Reasoning. Preprints 2024, 2024040849. https://doi.org/10.20944/preprints202404.0849.v1
PS, D.; PS, A.; VG, J. Intellecta Cognitiva: A Comprehensive Dataset for Advancing Academic Knowledge and Machine Reasoning. Preprints2024, 2024040849. https://doi.org/10.20944/preprints202404.0849.v1
APA Style
PS, D., PS, A., & VG, J. (2024). Intellecta Cognitiva: A Comprehensive Dataset for Advancing Academic Knowledge and Machine Reasoning. Preprints. https://doi.org/10.20944/preprints202404.0849.v1
Chicago/Turabian Style
PS, D., Ajmal PS and Jithin VG. 2024 "Intellecta Cognitiva: A Comprehensive Dataset for Advancing Academic Knowledge and Machine Reasoning" Preprints. https://doi.org/10.20944/preprints202404.0849.v1
Abstract
Intellecta dataset emerges as an innovative synthetic dataset, engineered to enhance the cognitive processing capabilities of contemporary language models. With a composition of 11.53 billion tokens, integrating 8.01 billion tokens of synthetic data with 3.52 billion tokens of rich textbook data, Intellecta is crafted to foster advanced reasoning and comprehensive educational narrative generation. Leveraging the Mixtral-8x7B-Instruct-v0.1 model, the dataset facilitates the generation of complex thought processes and detailed, textbook-style explanations, thus enabling language models to engage in both critical thinking and profound educational discourse. This hybrid dataset stands as a testament to the potential of synthetic data in pushing the boundaries of AI, offering a repository that is not only vast and varied but also refined to align with ethical standards and intellectual rigor.
Keywords
Synthetic data; pretrain data; llm training
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
