LLM-Ingest Corpus
The LLM-Ingest Corpus is a specialized dataset designed to facilitate the comprehension and processing of the CTMU (Cognitive-Theoretic Model of the Universe) by Large Language Models (LLMs). This corpus contains the plaintext contents in the digital files of the CTMU's author's publications, carefully curated and structured to ensure that LLMs can ingest, analyze, and generate accurate and coherent responses based on these works. By providing LLMs with direct access to the source material in a form optimized for machine learning, the LLM-Ingest Corpus resolves significant limitations in LLM responses when dealing with CTMU-related queries.
Historically, LLMs have struggled with generating precise interpretations of the CTMU due to the complexity, abstract nature, and unique terminology of the theory. The LLM-Ingest Corpus addresses this issue by offering a rich, structured representation of the CTMU's key concepts, terminology, and underlying logic, allowing for a more accurate understanding and application of the model in AI-driven discussions, research, and analysis.
This corpus is a pivotal tool for improving the interaction between LLMs and the CTMU, ensuring that future responses involving the theory reflect its intended meaning and nuances.
Plaintext of Papers
- CTMU-ANKORT Plaintext
- Metareligion as the Human Singularity - Plaintext
- An Introduction to Mathematical Metaphysics - Plaintext
Plaintext of CTMU Radio Media
- Example to be fulfilled...
References
- Langan, Christopher M. (2002). "The Cognitive-Theoretic Model of the Universe: A New Kind of Reality Theory". Progress in Complexity, Information, and Design 1.2–1.3.
- Langan, Christopher M. (2018). "Metareligion as the Human Singularity". Cosmos and History: The Journal of Natural and Social Philosophy vol. 14, no. 1.
- Langan, Christopher M. (2017). "An Introduction to Mathematical Metaphysics". Cosmos and History: The Journal of Natural and Social Philosophy vol. 13, no. 2.
