
LLM BS Detector
Lately, I have found myself diving into the deeply abstract and fascinating world of Category Theory. I will readily admit that much of the time I feel completely and hopelessly lost trying to navigate its complexities. However, despite the steep learning curve, dabbling in this field has given me a unique lens through which to view information. It has allowed me to sketch out a rough, intuitive understanding of what the "proper shape" and underlying structure of mathematically correct theories actually ought to look like.
This structural perspective becomes especially profound when evaluating artificial intelligence. Specifically, I find it incredibly interesting to analyze Large Language Models (LLMs) through this categorical framework. It highlights the stark philosophical difference between an AI generating a string of text based purely on probabilistic token prediction, as opposed to an AI actually formulating a grounded statement of objective truth. The ideas I'm sharing here are largely based on my own distillation of mathematical physicist John Baez’s work, recontextualized and applied to how we understand modern LLMs. I genuinely hope that people find this intersection of abstract math and AI thought-provoking and enjoy the concepts presented.