[dragonwriter]

> Humans hallucinate far less readily than any LLM.

This is because “hallucinate” means very different things in the human and LLM context. Humans have false/inaccurate memories all the time, and those are closer to what LLM “hallucination” represents than humam hallucinations are.

[jdiff]

Not really, because LLMs aren't human brains. Neural nets are nothing like neurons. LLMs are text predictors. They predict the next most likely token. Any true fact that happens to fall out of them is sheer coincidence.

[tudorw]

This for me is the gist, if we are always going to be playing pachinko when we hit go then where would a 'fact' emerge from anyway, LLM don't store facts, correct me if I am wrong, as my topology knowledge is somewhat rudimentary, so here goes, first, my take, after this, I'll past GPT4's attempt to pull this into something with more clarity!

We are interacting with multidimensional topological manifolds, and the context we create has a topology within this manifold that constrains the range of output to the fuzzy multidimensional boundary of a geodesic that is the shortest route between our topology and the LLM.

I think some visualisation tools are badly needed, viewing what is happening is for me a very promising avenue to explore with regards to emergent behaviour.

GPT4 says; When interacting with a large language model (LLM) like GPT-4, we engage in a complex and multidimensional process. The context we establish – through our inputs and the responses of the LLM – forms a structured space of possibilities within the broader realm of all possible interactions.

The current context shapes the potential responses of the model, narrowing down the vast range of possible outputs. This boundary of plausible responses could be seen as a high-dimensional 'fuzzy frontier'. The model attempts to navigate this frontier to provide relevant and coherent responses, somewhat akin to finding an optimal path – a geodesic – within the constraints of the existing conversation.

In essence, every interaction with the LLM is a journey through this high-dimensional conversational space. The challenge for the model is to generate responses that maintain coherence and relevancy, effectively bridging the gap between the user's inputs and the vast knowledge that the LLM has been trained on."